{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Appendix 10.2.5: Tool use with multiple tools\n",
    "\n",
    "Now we're going to take our tool use to another level! We're going to provide Claude with a suite of tools it can select from.  Our goal is to build a simple customer support chatbot for a fictional electronics company called TechNova. It will have access to simple tools including: \n",
    "\n",
    "* `get_user` to look up user details by email, username, or phone number\n",
    "* `get_order_by_id` to look up an order directly by its order ID\n",
    "* `get_customer_orders` to look up all the orders belonging to a customer\n",
    "* `cancel_order` cancels an order given a specific order ID\n",
    "\n",
    "This chatbot will be quite limited, but it demonstrates some key pieces of working with multiple tools. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's an image showcasing an example interaction with the chatbot.\n",
    "\n",
    "![conversation1.png](./images/conversation1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's another interaction with the chatbot where we've explicitly printed out a message any time Claude uses a tool, to make it easier to understand what's happening behind the scenes: \n",
    "\n",
    "![conversation2.png](./images/conversation2.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a diagram showing the flow of information for a potential single chat message:\n",
    "\n",
    "![chat_diagram.png](./images/chat_diagram.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**important note: this chatbot is extremely simple and allows anyone to cancel an order as long as they have the username or email of the user who placed the order. This implementation is for educational purposes and should not be integrated into a real codebase without alteration!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Our fake database\n",
    "Before we start working with Claude, we'll begin by defining a very simple `FakeDatabase` class that will hold some fake customers and orders, as well as providing methods for us to interact with the data. In the real world, the chatbot would presumably be connected to one or more *actual* databases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class FakeDatabase:\n",
    "    def __init__(self):\n",
    "        self.customers = [\n",
    "            {\"id\": \"1213210\", \"name\": \"John Doe\", \"email\": \"john@gmail.com\", \"phone\": \"123-456-7890\", \"username\": \"johndoe\"},\n",
    "            {\"id\": \"2837622\", \"name\": \"Priya Patel\", \"email\": \"priya@candy.com\", \"phone\": \"987-654-3210\", \"username\": \"priya123\"},\n",
    "            {\"id\": \"3924156\", \"name\": \"Liam Nguyen\", \"email\": \"lnguyen@yahoo.com\", \"phone\": \"555-123-4567\", \"username\": \"liamn\"},\n",
    "            {\"id\": \"4782901\", \"name\": \"Aaliyah Davis\", \"email\": \"aaliyahd@hotmail.com\", \"phone\": \"111-222-3333\", \"username\": \"adavis\"},\n",
    "            {\"id\": \"5190753\", \"name\": \"Hiroshi Nakamura\", \"email\": \"hiroshi@gmail.com\", \"phone\": \"444-555-6666\", \"username\": \"hiroshin\"},\n",
    "            {\"id\": \"6824095\", \"name\": \"Fatima Ahmed\", \"email\": \"fatimaa@outlook.com\", \"phone\": \"777-888-9999\", \"username\": \"fatimaahmed\"},\n",
    "            {\"id\": \"7135680\", \"name\": \"Alejandro Rodriguez\", \"email\": \"arodriguez@protonmail.com\", \"phone\": \"222-333-4444\", \"username\": \"alexr\"},\n",
    "            {\"id\": \"8259147\", \"name\": \"Megan Anderson\", \"email\": \"megana@gmail.com\", \"phone\": \"666-777-8888\", \"username\": \"manderson\"},\n",
    "            {\"id\": \"9603481\", \"name\": \"Kwame Osei\", \"email\": \"kwameo@yahoo.com\", \"phone\": \"999-000-1111\", \"username\": \"kwameo\"},\n",
    "            {\"id\": \"1057426\", \"name\": \"Mei Lin\", \"email\": \"meilin@gmail.com\", \"phone\": \"333-444-5555\", \"username\": \"mlin\"}\n",
    "        ]\n",
    "\n",
    "        self.orders = [\n",
    "            {\"id\": \"24601\", \"customer_id\": \"1213210\", \"product\": \"Wireless Headphones\", \"quantity\": 1, \"price\": 79.99, \"status\": \"Shipped\"},\n",
    "            {\"id\": \"13579\", \"customer_id\": \"1213210\", \"product\": \"Smartphone Case\", \"quantity\": 2, \"price\": 19.99, \"status\": \"Processing\"},\n",
    "            {\"id\": \"97531\", \"customer_id\": \"2837622\", \"product\": \"Bluetooth Speaker\", \"quantity\": 1, \"price\": \"49.99\", \"status\": \"Shipped\"}, \n",
    "            {\"id\": \"86420\", \"customer_id\": \"3924156\", \"product\": \"Fitness Tracker\", \"quantity\": 1, \"price\": 129.99, \"status\": \"Delivered\"},\n",
    "            {\"id\": \"54321\", \"customer_id\": \"4782901\", \"product\": \"Laptop Sleeve\", \"quantity\": 3, \"price\": 24.99, \"status\": \"Shipped\"},\n",
    "            {\"id\": \"19283\", \"customer_id\": \"5190753\", \"product\": \"Wireless Mouse\", \"quantity\": 1, \"price\": 34.99, \"status\": \"Processing\"},\n",
    "            {\"id\": \"74651\", \"customer_id\": \"6824095\", \"product\": \"Gaming Keyboard\", \"quantity\": 1, \"price\": 89.99, \"status\": \"Delivered\"},\n",
    "            {\"id\": \"30298\", \"customer_id\": \"7135680\", \"product\": \"Portable Charger\", \"quantity\": 2, \"price\": 29.99, \"status\": \"Shipped\"},\n",
    "            {\"id\": \"47652\", \"customer_id\": \"8259147\", \"product\": \"Smartwatch\", \"quantity\": 1, \"price\": 199.99, \"status\": \"Processing\"},\n",
    "            {\"id\": \"61984\", \"customer_id\": \"9603481\", \"product\": \"Noise-Cancelling Headphones\", \"quantity\": 1, \"price\": 149.99, \"status\": \"Shipped\"},\n",
    "            {\"id\": \"58243\", \"customer_id\": \"1057426\", \"product\": \"Wireless Earbuds\", \"quantity\": 2, \"price\": 99.99, \"status\": \"Delivered\"},\n",
    "            {\"id\": \"90357\", \"customer_id\": \"1213210\", \"product\": \"Smartphone Case\", \"quantity\": 1, \"price\": 19.99, \"status\": \"Shipped\"},\n",
    "            {\"id\": \"28164\", \"customer_id\": \"2837622\", \"product\": \"Wireless Headphones\", \"quantity\": 2, \"price\": 79.99, \"status\": \"Processing\"}\n",
    "        ]\n",
    "\n",
    "    def get_user(self, key, value):\n",
    "        if key in {\"email\", \"phone\", \"username\"}:\n",
    "            for customer in self.customers:\n",
    "                if customer[key] == value:\n",
    "                    return customer\n",
    "            return f\"Couldn't find a user with {key} of {value}\"\n",
    "        else:\n",
    "            raise ValueError(f\"Invalid key: {key}\")\n",
    "\n",
    "        return None\n",
    "\n",
    "    def get_order_by_id(self, order_id):\n",
    "        for order in self.orders:\n",
    "            if order[\"id\"] == order_id:\n",
    "                return order\n",
    "        return None\n",
    "\n",
    "    def get_customer_orders(self, customer_id):\n",
    "        return [order for order in self.orders if order[\"customer_id\"] == customer_id]\n",
    "\n",
    "    def cancel_order(self, order_id):\n",
    "        order = self.get_order_by_id(order_id)\n",
    "        if order:\n",
    "            if order[\"status\"] == \"Processing\":\n",
    "                order[\"status\"] = \"Cancelled\"\n",
    "                return \"Cancelled the order\"\n",
    "            else:\n",
    "                return \"Order has already shipped.  Can't cancel it.\"\n",
    "        return \"Can't find that order!\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll create an instance of the `FakeDatabase`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "db = FakeDatabase()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make sure our `get_user` method works as intended.  It's expecting us to pass in a `key` that is one of: \n",
    "\n",
    "* \"email\"\n",
    "* \"username\"\n",
    "* \"phone\" \n",
    "\n",
    "It also expects a corresponding `value` that it will use to perform a basic search, hopefully returning the matching user."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "db.get_user(\"email\", \"john@gmail.com\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "db.get_user(\"username\", \"adavis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "db.get_user(\"phone\", \"666-777-8888\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also get a list of all orders that belong to a particular customer by calling `get_customer_orders` and passing in a customer ID:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "db.get_customer_orders(\"1213210\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "db.get_customer_orders(\"9603481\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also can look up a single order directly if we have the order ID:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "db.get_order_by_id('24601')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we can use the `cancel_order` method to cancel an order. Orders can only be cancelled if they are \"processing\". We can't cancel an order that has already shipped!  The method expects us to pass in an order_id of the order we want to cancel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Let's look up an order that has a status of processing:\n",
    "db.get_order_by_id(\"47652\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Now let's cancel it! \n",
    "db.cancel_order(\"47652\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# It's status should now be \"Cancelled\"\n",
    "db.get_order_by_id(\"47652\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing our tools\n",
    "\n",
    "Now that we've tested our (very) simple fake database functionality, let's write JSON schemas that define the structure of the tools. Let's take a look at a very simple tool that corresponds to our `get_order_by_id` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "tool1 = {\n",
    "      \"toolSpec\": {\n",
    "        \"name\": \"get_order_by_id\",\n",
    "        \"description\": \"Retrieves the details of a specific order based on the order ID. Returns the order ID, product name, quantity, price, and order status.\",\n",
    "        \"inputSchema\": {\n",
    "          \"json\": {\n",
    "            \"type\": \"object\",\n",
    "            \"properties\": {\n",
    "              \"order_id\": {\n",
    "                \"type\": \"string\",\n",
    "                \"description\": \"The unique identifier for the order.\"}\n",
    "            },\n",
    "            \"required\": [\"order_id\"]\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As always, we include a name, a description, and an overview of the inputs.  In this case, there is a single input, `order_id`, and it is required. \n",
    "\n",
    "Now let's take a look at a more complex tool that corresponds to the `get_user` method.  Recall that this method has two arguments:\n",
    "\n",
    "1. A `key` argument that is one of the following strings:\n",
    "    * \"email\"\n",
    "    * \"username\"\n",
    "    * \"phone\" \n",
    "2. A `value` argument which is the term we'll be using to search (the actual email, phone number, or username)\n",
    "\n",
    "Here's the corresponding tool definition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "tool2 = {\n",
    "      \"toolSpec\": {\n",
    "        \"name\": \"get_user\",\n",
    "        \"description\": \"Looks up a user by email, phone, or username.\",\n",
    "        \"inputSchema\": {\n",
    "          \"json\": {\n",
    "            \"type\": \"object\",\n",
    "            \"properties\": {\n",
    "              \"key\": {\n",
    "                \"type\": \"string\",\n",
    "                \"enum\": [\"email\", \"phone\", \"username\"],\n",
    "                \"description\": \"The attribute to search for a user by (email, phone, or username).\"},\n",
    "              \"value\": {\n",
    "                \"type\": \"string\",\n",
    "                \"description\": \"The value to match for the specified attribute.\"}\n",
    "            },\n",
    "            \"required\": [\"key\", \"value\"]\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pay special attention to the way we define the set of possible valid options for `key` using `enum` in the schema."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We still have two more tools to write, but to save time, we'll just provide you the complete list of tools ready for us to use. (You're welcome to try defining them youself first as an exercise!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "toolConfig = {\n",
    "  'tools': [\n",
    "    {\n",
    "      'toolSpec': {\n",
    "        'name': 'get_user',\n",
    "        'description': 'Looks up a user by email, phone, or username.',\n",
    "        'inputSchema': {\n",
    "          'json': {\n",
    "            'type': 'object',\n",
    "            'properties': {\n",
    "              'key': {\n",
    "                'type': 'string',\n",
    "                'enum': ['email', 'phone', 'username'],\n",
    "                'description': 'The attribute to search for a user by (email, phone, or username).'\n",
    "              },\n",
    "              'value': {\n",
    "                'type': 'string',\n",
    "                'description': 'The value to match for the specified attribute.'\n",
    "              }\n",
    "            },\n",
    "            'required': ['key', 'value']\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "    },\n",
    "    {\n",
    "      'toolSpec': {\n",
    "        'name': 'get_order_by_id',\n",
    "        'description': 'Retrieves the details of a specific order based on the order ID. Returns the order ID, product name, quantity, price, and order status.',\n",
    "        'inputSchema': {\n",
    "          'json': {\n",
    "            'type': 'object',\n",
    "            'properties': {\n",
    "              'order_id': {\n",
    "                'type': 'string',\n",
    "                'description': 'The unique identifier for the order.'\n",
    "              }\n",
    "            },\n",
    "            'required': ['order_id']\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "    },\n",
    "    {\n",
    "      'toolSpec': {\n",
    "        'name': 'get_customer_orders',\n",
    "        'description': \"Retrieves the list of orders belonging to a user based on a user's customer id.\",\n",
    "        'inputSchema': {\n",
    "          'json': {\n",
    "            'type': 'object',\n",
    "            'properties': {\n",
    "              'customer_id': {\n",
    "                'type': 'string',\n",
    "                'description': 'The customer_id belonging to the user'\n",
    "              }\n",
    "            },\n",
    "            'required': ['customer_id']\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "    },\n",
    "    {\n",
    "      'toolSpec': {\n",
    "        'name': 'cancel_order',\n",
    "        'description': \"Cancels an order based on a provided order_id.  Only orders that are 'processing' can be cancelled\",\n",
    "        'inputSchema': {\n",
    "          'json': {\n",
    "            'type': 'object',\n",
    "            'properties': {\n",
    "              'order_id': {\n",
    "                'type': 'string',\n",
    "                'description': 'The order_id pertaining to a particular order'\n",
    "              }\n",
    "            },\n",
    "            'required': ['order_id']\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "    }\n",
    "  ],\n",
    "  'toolChoice': {\n",
    "    'auto': {}\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Giving our tools to Claude\n",
    "\n",
    "Next up, we need to do a few things: \n",
    "1. Tell Claude about our tools and send the user's chat message to Claude.\n",
    "2. Handle the response we get back from Claude:\n",
    "    * If Claude doesn't want to use a tool:\n",
    "        * Print Claude's output to the user.\n",
    "        * Ask the user for their next message.\n",
    "    * If Claude does want to use a tool\n",
    "        * Verify that Claude called one of the appropriate tools.\n",
    "        * Execute the underlying function, like `get_user` or `cancel_order`.\n",
    "        * Send Claude the result of running the tool."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eventually, the goal is to write a command line script that will create an interactive chatbot that will run in a loop. But to keep things simple, we'll start by writing the basic code linearly.  We'll end by creating an interactive chatbot script."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start with a function that can translate Claude's tool use response into an actual method call on our `db`: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def process_tool_call(tool_name, tool_input):\n",
    "    if tool_name == \"get_user\":\n",
    "        return db.get_user(tool_input[\"key\"], tool_input[\"value\"])\n",
    "    elif tool_name == \"get_order_by_id\":\n",
    "        return db.get_order_by_id(tool_input[\"order_id\"])\n",
    "    elif tool_name == \"get_customer_orders\":\n",
    "        return db.get_customer_orders(tool_input[\"customer_id\"])\n",
    "    elif tool_name == \"cancel_order\":\n",
    "        return db.cancel_order(tool_input[\"order_id\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with a simple demo that shows Claude can decide which tool to use when presented with a list of multiple tools. We'll ask Claude `Can you look up my orders? My email is john@gmail.com` and see what happens!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import boto3\n",
    "import json\n",
    "from datetime import datetime\n",
    "from botocore.exceptions import ClientError\n",
    "\n",
    "session = boto3.Session()\n",
    "region = session.region_name\n",
    "\n",
    "modelId = 'anthropic.claude-3-sonnet-20240229-v1:0'\n",
    "\n",
    "print(f'Using modelId: {modelId}')\n",
    "print('Using region: ', region)\n",
    "\n",
    "bedrock_client = boto3.client(service_name = 'bedrock-runtime', region_name = region,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "messages = [{\"role\": \"user\", \"content\": [{\"text\": \"Can you look up my orders? My email is john@gmail.com\"}]}]\n",
    "\n",
    "converse_api_params = {\n",
    "    \"modelId\": modelId,\n",
    "    \"messages\": messages,\n",
    "    \"inferenceConfig\": {\"maxTokens\": 4096},\n",
    "    \"toolConfig\":toolConfig,\n",
    "}\n",
    "\n",
    "response = bedrock_client.converse(**converse_api_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "print(response['output']['message']['content'][-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Claude wants to use the `get_user` tool.\n",
    "\n",
    "Now we'll write the basic logic to update our messages list, call the appropriate tool, and update our messages list again with the tool results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Update messages to include Claude's response\n",
    "messages.append(\n",
    "    {\"role\": \"assistant\", \"content\": response['output']['message']['content']}\n",
    ")\n",
    "\n",
    "tool_use = response['output']['message']['content'][-1]\n",
    "tool_id = tool_use['toolUse']['toolUseId']\n",
    "tool_name = tool_use['toolUse']['name']\n",
    "tool_input = tool_use['toolUse']['input']\n",
    "\n",
    "#If Claude stops because it wants to use a tool:\n",
    "if response['stopReason'] == \"tool_use\":\n",
    "    tool_use = response['output']['message']['content'][-1] #Naive approach assumes only 1 tool is called at a time\n",
    "    tool_id = tool_use['toolUse']['toolUseId']\n",
    "    tool_name = tool_use['toolUse']['name']\n",
    "    tool_input = tool_use['toolUse']['input']\n",
    "\n",
    "    print(f\"Claude wants to use the {tool_name} tool\")\n",
    "    print(f\"Tool Input:\")\n",
    "    print(json.dumps(tool_input, indent=2))\n",
    "\n",
    "    #Actually run the underlying tool functionality on our db\n",
    "    tool_result = process_tool_call(tool_name, tool_input)\n",
    "\n",
    "    print(f\"\\nTool Result:\")\n",
    "    print(json.dumps(tool_result, indent=2))\n",
    "\n",
    "    #Add our tool_result message:\n",
    "    messages.append({\n",
    "        \"role\": \"user\",\n",
    "        \"content\": [\n",
    "            {\n",
    "                \"toolResult\": {\n",
    "                    \"toolUseId\": tool_id,\n",
    "                    \"content\": [\n",
    "                        {\"text\": str(tool_result)}\n",
    "                    ]\n",
    "                }\n",
    "            }\n",
    "        ]\n",
    "    })\n",
    "\n",
    "else: \n",
    "    #If Claude does NOT want to use a tool, just print out the text reponse\n",
    "    print(\"\\nTechNova Support:\" + f\"response['output']['message']['content'][0]['text']\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is what our messages list looks like now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "messages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll send a second request to Claude using the updated messages list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "converse_api_params = {\n",
    "    \"modelId\": modelId,\n",
    "    \"messages\": messages,\n",
    "    \"inferenceConfig\": {\"maxTokens\": 4096},\n",
    "    \"toolConfig\":toolConfig,\n",
    "}\n",
    "\n",
    "response2 = bedrock_client.converse(**converse_api_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "response2['output']['message']['content'][-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that Claude has the result of the `get_user` tool, including the user's ID, it wants to call `get_customer_orders` to look up the orders that correspond to this particular customer.  It's picking the right tool for the job. **Note:** there are many potential issues and places Claude could go wrong here, but this is a start!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing an interactive script\n",
    "\n",
    "It's a lot easier to interact with this chatbot via an interactive command line script. \n",
    "\n",
    "Here's all of the code from above combined into a single function that starts a loop-based chat session (you'll probably want to run this as its own script from the command line):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "import json\n",
    "from datetime import datetime\n",
    "from botocore.exceptions import ClientError\n",
    "\n",
    "session = boto3.Session()\n",
    "region = session.region_name\n",
    "\n",
    "modelId = 'anthropic.claude-3-sonnet-20240229-v1:0'\n",
    "\n",
    "bedrock_client = boto3.client(service_name = 'bedrock-runtime', region_name = region,)\n",
    "\n",
    "class FakeDatabase:\n",
    "    def __init__(self):\n",
    "        self.customers = [\n",
    "            {\"id\": \"1213210\", \"name\": \"John Doe\", \"email\": \"john@gmail.com\", \"phone\": \"123-456-7890\", \"username\": \"johndoe\"},\n",
    "            {\"id\": \"2837622\", \"name\": \"Priya Patel\", \"email\": \"priya@candy.com\", \"phone\": \"987-654-3210\", \"username\": \"priya123\"},\n",
    "            {\"id\": \"3924156\", \"name\": \"Liam Nguyen\", \"email\": \"lnguyen@yahoo.com\", \"phone\": \"555-123-4567\", \"username\": \"liamn\"},\n",
    "            {\"id\": \"4782901\", \"name\": \"Aaliyah Davis\", \"email\": \"aaliyahd@hotmail.com\", \"phone\": \"111-222-3333\", \"username\": \"adavis\"},\n",
    "            {\"id\": \"5190753\", \"name\": \"Hiroshi Nakamura\", \"email\": \"hiroshi@gmail.com\", \"phone\": \"444-555-6666\", \"username\": \"hiroshin\"},\n",
    "            {\"id\": \"6824095\", \"name\": \"Fatima Ahmed\", \"email\": \"fatimaa@outlook.com\", \"phone\": \"777-888-9999\", \"username\": \"fatimaahmed\"},\n",
    "            {\"id\": \"7135680\", \"name\": \"Alejandro Rodriguez\", \"email\": \"arodriguez@protonmail.com\", \"phone\": \"222-333-4444\", \"username\": \"alexr\"},\n",
    "            {\"id\": \"8259147\", \"name\": \"Megan Anderson\", \"email\": \"megana@gmail.com\", \"phone\": \"666-777-8888\", \"username\": \"manderson\"},\n",
    "            {\"id\": \"9603481\", \"name\": \"Kwame Osei\", \"email\": \"kwameo@yahoo.com\", \"phone\": \"999-000-1111\", \"username\": \"kwameo\"},\n",
    "            {\"id\": \"1057426\", \"name\": \"Mei Lin\", \"email\": \"meilin@gmail.com\", \"phone\": \"333-444-5555\", \"username\": \"mlin\"}\n",
    "        ]\n",
    "\n",
    "        self.orders = [\n",
    "            {\"id\": \"24601\", \"customer_id\": \"1213210\", \"product\": \"Wireless Headphones\", \"quantity\": 1, \"price\": 79.99, \"status\": \"Shipped\"},\n",
    "            {\"id\": \"13579\", \"customer_id\": \"1213210\", \"product\": \"Smartphone Case\", \"quantity\": 2, \"price\": 19.99, \"status\": \"Processing\"},\n",
    "            {\"id\": \"97531\", \"customer_id\": \"2837622\", \"product\": \"Bluetooth Speaker\", \"quantity\": 1, \"price\": \"49.99\", \"status\": \"Shipped\"}, \n",
    "            {\"id\": \"86420\", \"customer_id\": \"3924156\", \"product\": \"Fitness Tracker\", \"quantity\": 1, \"price\": 129.99, \"status\": \"Delivered\"},\n",
    "            {\"id\": \"54321\", \"customer_id\": \"4782901\", \"product\": \"Laptop Sleeve\", \"quantity\": 3, \"price\": 24.99, \"status\": \"Shipped\"},\n",
    "            {\"id\": \"19283\", \"customer_id\": \"5190753\", \"product\": \"Wireless Mouse\", \"quantity\": 1, \"price\": 34.99, \"status\": \"Processing\"},\n",
    "            {\"id\": \"74651\", \"customer_id\": \"6824095\", \"product\": \"Gaming Keyboard\", \"quantity\": 1, \"price\": 89.99, \"status\": \"Delivered\"},\n",
    "            {\"id\": \"30298\", \"customer_id\": \"7135680\", \"product\": \"Portable Charger\", \"quantity\": 2, \"price\": 29.99, \"status\": \"Shipped\"},\n",
    "            {\"id\": \"47652\", \"customer_id\": \"8259147\", \"product\": \"Smartwatch\", \"quantity\": 1, \"price\": 199.99, \"status\": \"Processing\"},\n",
    "            {\"id\": \"61984\", \"customer_id\": \"9603481\", \"product\": \"Noise-Cancelling Headphones\", \"quantity\": 1, \"price\": 149.99, \"status\": \"Shipped\"},\n",
    "            {\"id\": \"58243\", \"customer_id\": \"1057426\", \"product\": \"Wireless Earbuds\", \"quantity\": 2, \"price\": 99.99, \"status\": \"Delivered\"},\n",
    "            {\"id\": \"90357\", \"customer_id\": \"1213210\", \"product\": \"Smartphone Case\", \"quantity\": 1, \"price\": 19.99, \"status\": \"Shipped\"},\n",
    "            {\"id\": \"28164\", \"customer_id\": \"2837622\", \"product\": \"Wireless Headphones\", \"quantity\": 2, \"price\": 79.99, \"status\": \"Processing\"}\n",
    "        ]\n",
    "\n",
    "    def get_user(self, key, value):\n",
    "        if key in {\"email\", \"phone\", \"username\"}:\n",
    "            for customer in self.customers:\n",
    "                if customer[key] == value:\n",
    "                    return customer\n",
    "            return f\"Couldn't find a user with {key} of {value}\"\n",
    "        else:\n",
    "            raise ValueError(f\"Invalid key: {key}\")\n",
    "        \n",
    "        return None\n",
    "\n",
    "    def get_order_by_id(self, order_id):\n",
    "        for order in self.orders:\n",
    "            if order[\"id\"] == order_id:\n",
    "                return order\n",
    "        return None\n",
    "    \n",
    "    def get_customer_orders(self, customer_id):\n",
    "        return [order for order in self.orders if order[\"customer_id\"] == customer_id]\n",
    "\n",
    "    def cancel_order(self, order_id):\n",
    "        order = self.get_order_by_id(order_id)\n",
    "        if order:\n",
    "            if order[\"status\"] == \"Processing\":\n",
    "                order[\"status\"] = \"Cancelled\"\n",
    "                return \"Cancelled the order\"\n",
    "            else:\n",
    "                return \"Order has already shipped.  Can't cancel it.\"\n",
    "        return \"Can't find that order!\"\n",
    "\n",
    "toolConfig = {\n",
    "  'tools': [\n",
    "    {\n",
    "      'toolSpec': {\n",
    "        'name': 'get_user',\n",
    "        'description': 'Looks up a user by email, phone, or username.',\n",
    "        'inputSchema': {\n",
    "          'json': {\n",
    "            'type': 'object',\n",
    "            'properties': {\n",
    "              'key': {\n",
    "                'type': 'string',\n",
    "                'enum': ['email', 'phone', 'username'],\n",
    "                'description': 'The attribute to search for a user by (email, phone, or username).'\n",
    "              },\n",
    "              'value': {\n",
    "                'type': 'string',\n",
    "                'description': 'The value to match for the specified attribute.'\n",
    "              }\n",
    "            },\n",
    "            'required': ['key', 'value']\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "    },\n",
    "    {\n",
    "      'toolSpec': {\n",
    "        'name': 'get_order_by_id',\n",
    "        'description': 'Retrieves the details of a specific order based on the order ID. Returns the order ID, product name, quantity, price, and order status.',\n",
    "        'inputSchema': {\n",
    "          'json': {\n",
    "            'type': 'object',\n",
    "            'properties': {\n",
    "              'order_id': {\n",
    "                'type': 'string',\n",
    "                'description': 'The unique identifier for the order.'\n",
    "              }\n",
    "            },\n",
    "            'required': ['order_id']\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "    },\n",
    "    {\n",
    "      'toolSpec': {\n",
    "        'name': 'get_customer_orders',\n",
    "        'description': \"Retrieves the list of orders belonging to a user based on a user's customer id.\",\n",
    "        'inputSchema': {\n",
    "          'json': {\n",
    "            'type': 'object',\n",
    "            'properties': {\n",
    "              'customer_id': {\n",
    "                'type': 'string',\n",
    "                'description': 'The customer_id belonging to the user'\n",
    "              }\n",
    "            },\n",
    "            'required': ['customer_id']\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "    },\n",
    "    {\n",
    "      'toolSpec': {\n",
    "        'name': 'cancel_order',\n",
    "        'description': \"Cancels an order based on a provided order_id.  Only orders that are 'processing' can be cancelled\",\n",
    "        'inputSchema': {\n",
    "          'json': {\n",
    "            'type': 'object',\n",
    "            'properties': {\n",
    "              'order_id': {\n",
    "                'type': 'string',\n",
    "                'description': 'The order_id pertaining to a particular order'\n",
    "              }\n",
    "            },\n",
    "            'required': ['order_id']\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "    }\n",
    "  ],\n",
    "  'toolChoice': {\n",
    "    'auto': {}\n",
    "  }\n",
    "}\n",
    "\n",
    "db = FakeDatabase()\n",
    "\n",
    "def process_tool_call(tool_name, tool_input):\n",
    "    if tool_name == \"get_user\":\n",
    "        return db.get_user(tool_input[\"key\"], tool_input[\"value\"])\n",
    "    elif tool_name == \"get_order_by_id\":\n",
    "        return db.get_order_by_id(tool_input[\"order_id\"])\n",
    "    elif tool_name == \"get_customer_orders\":\n",
    "        return db.get_customer_orders(tool_input[\"customer_id\"])\n",
    "    elif tool_name == \"cancel_order\":\n",
    "        return db.cancel_order(tool_input[\"order_id\"])\n",
    "\n",
    "def simple_chat():\n",
    "    user_message = input(\"\\nUser: \")\n",
    "    messages = [{\"role\": \"user\", \"content\": [{\"text\": user_message}]}]\n",
    "    #messages = [{\"role\": \"user\", \"content\": user_message}]\n",
    "    while True:\n",
    "        #If the last message is from the assistant, get another input from the user\n",
    "        if messages[-1].get(\"role\") == \"assistant\":\n",
    "            user_message = input(\"\\nUser: \")\n",
    "            messages.append({\"role\": \"user\", \"content\": [{\"text\": user_message}]})\n",
    "\n",
    "        converse_api_params = {\n",
    "            \"modelId\": modelId,\n",
    "            \"messages\": messages,\n",
    "            \"inferenceConfig\": {\"maxTokens\": 4096},\n",
    "            \"toolConfig\":toolConfig,\n",
    "        }\n",
    "\n",
    "        response = bedrock_client.converse(**converse_api_params)\n",
    "\n",
    "        messages.append({\"role\": \"assistant\", \"content\": response['output']['message']['content']})\n",
    "\n",
    "        #If Claude stops because it wants to use a tool:\n",
    "        if response['stopReason'] == \"tool_use\":\n",
    "            tool_use = response['output']['message']['content'][-1] #Naive approach assumes only 1 tool is called at a time\n",
    "            tool_id = tool_use['toolUse']['toolUseId']\n",
    "            tool_name = tool_use['toolUse']['name']\n",
    "            tool_input = tool_use['toolUse']['input']\n",
    "\n",
    "            print(f\"Claude wants to use the {tool_name} tool\")\n",
    "            print(f\"Tool Input:\")\n",
    "            print(json.dumps(tool_input, indent=2))\n",
    "\n",
    "            #Actually run the underlying tool functionality on our db\n",
    "            tool_result = process_tool_call(tool_name, tool_input)\n",
    "\n",
    "            print(f\"\\nTool Result:\")\n",
    "            print(json.dumps(tool_result, indent=2))\n",
    "\n",
    "            #Add our tool_result message:\n",
    "            messages.append({\n",
    "                \"role\": \"user\",\n",
    "                \"content\": [\n",
    "                    {\n",
    "                        \"toolResult\": {\n",
    "                            \"toolUseId\": tool_id,\n",
    "                            \"content\": [\n",
    "                                {\"text\": str(tool_result)}\n",
    "                            ]\n",
    "                        }\n",
    "                    }\n",
    "                ]\n",
    "            })\n",
    "\n",
    "        else: \n",
    "            #If Claude does NOT want to use a tool, just print out the text reponse\n",
    "            print(\"\\nTechNova Support:\" + f\"{response['output']['message']['content'][0]['text']}\")\n",
    "\n",
    "# Start the chat!!\n",
    "# simple_chat()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's an example conversation:\n",
    "\n",
    "![conversation3.png](./images/conversation3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the chatbot is calling the correct tools when needed, but the actual chat responses are not ideal.  We probably don't want a customer-facing chatbot telling users about the specific tools it's going to call!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prompt enhancements\n",
    "\n",
    "We can improve the chat experience by providing our chatbot with a solid system prompt.  One of the first things we should do is give Claude some context and tell it that it's acting as a customer service assistant for TechNova.\n",
    "\n",
    "Here's a start for our system prompt:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "system_prompt = \"\"\"\n",
    "You are a customer support chat bot for an online retailer called TechNova. \n",
    "Your job is to help users look up their account, orders, and cancel orders.\n",
    "Be helpful and brief in your responses.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't forget to pass this prompt into the `system` parameter when making requests to Claude!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a sample conversation after adding in the above system prompt details:\n",
    "\n",
    "![conversation4.png](./images/conversation4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the assistant knows it is a support bot for TechNova, and it no longer tells the user about its tools.  It might also be worth explicitly telling Claude not to reference tools at all.\n",
    "\n",
    "**Note:** The `=======Claude wants to use the cancel_order_tool=======` lines are logging we added to the script, not Claude's actual outputs!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you play with this script enough, you'll likely notice other issues.  One very obvious and very problematic issue is illustrated by the following exchange: \n",
    "\n",
    "![conversation5.png](./images/conversation5.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The screenshot above shows the entire conversation. The user asks for help canceling an order and states that they don't know the order ID. Claude should follow up and ask for the customer's email, phone number, or username to look up the customer and then find the matching orders. Instead, Claude just decides to call the `get_user` tool without actually knowing any information about the user. Claude made up an email address and tried to use it, but of course didn't find a matching customer.\n",
    "\n",
    "To prevent this, we'll update the system prompt to include language along the lines of:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "system_prompt = \"\"\"\n",
    "You are a customer support chat bot for an online retailer called TechNova. \n",
    "Your job is to help users look up their account, orders, and cancel orders.\n",
    "Be helpful and brief in your responses.\n",
    "You have access to a set of tools, but only use them when needed.  \n",
    "If you do not have enough information to use a tool correctly, ask a user follow up questions to get the required inputs.\n",
    "Do not call any of the tools unless you have the required data from a user. \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a screenshot of a conversation with the assistant that uses the updated system prompt:\n",
    "\n",
    "![conversation6.png](./images/conversation6.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much better!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "attachments": {
    "conversation7.png": {
     "image/png": 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+U2NyZWVuc2hvdDwvZXhpZjpVc2VyQ29tbWVudD4KICAgICAgPC9yZGY6RGVzY3JpcHRpb24+CiAgIDwvcmRmOlJERj4KPC94OnhtcG1ldGE+CgIHbDIAAAAcaURPVAAAAAIAAAAAAAACHQAAACgAAAIdAAACHQABq7w5EpFoAABAAElEQVR4AezdC7xd04E/8CUTEuqZiKhWxCMolf6RTlszWoZO/y0hxDBVRKr405Sgod6vMNJpGtpKSuv9DlHj0b9+mvxNyxhCvZMgEeodggSVCpV/157Ze/bZOefec+/Z595z47s/n/bsx9prr/3d6+xzT/zO2iutv/76y4KJAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQBcJrCTA2kXSDkOAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJAICLDqCAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAl0qIMDapdwORoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgIMCqDxAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECHSpgABrl3I7GAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgACrPkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINClAgKsXcrtYAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgKs+gABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECXCgiwdim3gxEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECAiw6gMECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJdKiDA2qXcDkaAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICDAqg8QIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAh0qYAAa5dyOxgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIAAqz5AgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQpQICrF3K7WAECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQICrPoAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAlwoIsHYpt4MRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgIsOoDBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECXSogwNql3A5GgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAgwKoPECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIdKmAAGuXcjsYAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQICAAKs+QIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0KUCAqxdyu1gBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECAqz6AAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQJcKCLB2KbeDESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQICLDqAwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAl0qIMDapdwORoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgIMCqDxAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECHSpgABrl3I7GAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgACrPkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINClAgKsXcrtYAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgKs+gABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECXCgiwdim3gxEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECAiw6gMECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJdKiDA2qXcDkaAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICDAqg8QIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAh0qYAAa5dyOxgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIAAqz5AgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQpQICrF3K7WAECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQICrPpAywhMnDgxa8uNN94Y7rvvvmy5p85ccMEFYdVVV02aP3369DB16tSeeiraTYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEShNoKMB6zDHHhI022ihrzNlnnx3eeOONbLk4M2jQoHDsscdmq+++++4wbdq0bNlMawmcdtppoV+/fkmjPvjggzBu3Lg2Gzhw4MBw4oknZmXmz58ffvKTn2TL7c3cfvvtWZEYXh0/fny23FNnbr311tCrV6+k+fPmzQtjx47tqafSUu2eMGFC6NOnT9KmRYsWhTPOOKOl2qcxBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINC2QEMB1iuvvDILOMbDHHnkkeH555+vecSRI0eG0aNHZ9sfeuihEEOSptYUuOWWW0Lv3r2Txi1btiwMHz68zYYOGTIkTJo0KSuzcOHCcPDBB2fL7c20YoB18uTJYZVVVkmaPnPmzHDxxRe3dxoV2wVYKzhKW8j3laVLl4a99967tLpVRIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLNFxBgbb5xjz2CAGsIt912W1hppZWSazhnzpx2R6EtXmwB1qJIOcsCrOU4qoUAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLdJSDA2l3yPeC4XR1gveKKK7IRX6dPnx4uu+yyblcSYO32S1C1AQKsVVmsJECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQI8REGDtMZeq6xva1QHWrj/D9o8owNq+UXeUEGDtDnXHJECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQHkCAqzlWa5wNQmwhiDA2prdWoC1Na+LVhEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBegZYPsO6///5hiy22CAMGDAhrrrlm+Mtf/hLefvvtsGjRovDggw+Gf/u3f6v3XJNyn/nMZ8J2220Xttxyy7DhhhuGjz76KLz44oth1qxZ4Z577gkvvfRS3fWNHDkyKxsfeb948eJkediwYWGvvfYK6623XlhttdXCO++8E1599dUwderUMHv27GyfajM777xzcp75ba+//nq4995786u6ZL6ZAdahQ4eG/v371zyPZ599Njz33HM1tzdjQ/56pvWPHj06nQ2vvfZauOOOO7Ll/Mwf//jHpD/m18X5W2+9NfTq1StZPW/evDB27NhkftSoUSH2k7XWWivpgy+88EJ45JFHwrRp05Lt9f5fq/fnMtuXNxFgzWuYJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQM8TaNkAawz4jRgxIqy88srtqsbg6XnnndduucmTJ4dBgwbVLLds2bJwww03hKuvvrpmmXTDpz71qXDRRReli2HGjBlh0qRJ4dJLL02Cq9mGwsxDDz0UTjvttMLa/1nMB/PStW+88UaIHl09NTPAmq+72nndd999Yfz48dU2NW1dNft6DxbDtmPGjFmueDHAOmHChBD7Ya1+/fLLL4fDDjtsuXqqrWj1/lxm+4rnn79WS5cuDXvvvXexiGUCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQaGGBlgywfvvb3+5QIK1WeDB133HHHcOxxx5bMzSYlktf8yNlpuuKr8UAawzRbrvttuETn/hEsWjFchzFM55frSkfzEvLCLCmEs19rWZf7xFr9cF8gDWO7htHne3bt2+b1b7yyivh0EMPrVmm1ftzM9pXxMhfKwHWoo5lAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQItL5ASwZY8+G0SLh48eJw//33h1dffTUJ/2200UZhww03DBtssEEiXCs8GDcOHDgw/PKXvwwrrbRSUjb+30cffRSeeuqpMH/+/KS+IUOGLDcya3yU+2WXXZbtU5wpBlhjnemj4mPZDz74ILz55pvhvffeC/369Qtrrrlm0gYB1v+SPPPMM8Maa6yRsUa7zTbbLFvujhFYjznmmORaZY3460wMJadTvJax31SbHn/88TB16tTlNuUDrPmNsR8888wzSZ/Zeuutw+qrr57fHGJb5s6dW7EuLrR6f25W+4oQ+XuEAGtRxzIBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRaX6DlAqwHHHBA+Od//udMLgYDTzzxxGw5PxPDcnGkyrfeeitceOGF+U3Z/Pnnn18RjIzBwVjfggULsjJxZpdddglHH310FkKNgdQDDzwwCc9WFPzvhWKANS3z4YcfhgsuuCDcdddd6arkdd111w3HH398ElQ88sgjK7blF/LBvHR9K4zAGtuybNmytEk1X/NB4YULF4aDDz64Ztnihvy5d0eAtdieuHzbbbdl4ec5c+aEcePGVStWc10xwBoNL7/88hAD0vnpZz/7WRg8eHC26rHHHgsnnXRStpzOtHp/blb70vNPX/N9RYA1VfFKgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBniPQcgHWs846K2y33XaZYAwMxuBgZ6Zhw4aFM844I9v1z3/+c9hnn32y5eJMMTx77733hnPPPbdYLFmuFmCNo67GQG0MbnZ2ygfz0jpaJcCatqfeVwHWEIoB1lrB3GJ/qjZSb6v352a2r9jn8u+T999/P4wcObJYxDIBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQItLBAywVYY+A0BuHS6eabbw6XXnpputih14kTJ4Ytttgi22fChAnh7rvvzparzdx0002hb9++yaZqIcJ0n2LgMK5vpK1pvflgXrpOgDWV6PrXskdgHTVqVIjXs9p03XXXhTXWWCPZ9N5774V99923olir9+dmtq8C4q8L+etSzapY3jIBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQItJZAywVYY8Dvn/7pnzKlOLriOeecEx566KFsXb0zV111VVhnnXWy4vHR7fER7umUf9x9r169ktV77LFHWHvttZP5Dz/8MIwYMSItXvFaDLC2VbZix3YWhg4dGtZcc82KUm+++WaYPXt2xbquWLjllltC7969s0Nde+212Xy1mQ033DDsuOOO2SYjsFaOwNreCMC//OUvw/rrr5/4VetPrd6fm9m+rFP990y+b7711lvhwAMPLBaxTIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQItLNByAdZBgwaFCy+8MOTDpdFvyZIl4dlnnw0PP/xweOCBB8K8efPaZc2H3NotXKNArREziwHWjoY1axyupVbn/WLwd/jw4W22b8iQIWHSpElZmY6a5Eefve+++8L48eOzurprJj/S55w5c8K4ceM61JRbb701pOHotkb0jZWef/75YbPNNkvqr+advx4dakSucDP7czPblzuFZDY/UvLLL78cDjvssGIRywQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQwgItF2CNVkceeWT4xje+0SbbBx98kIxKGkN/r7/+etWy+UBk1QJ1rIxtef7555crWQywPvXUU+G4445brlxPXpEPJFYLVBbPTYC1KFI5Auv8+fPDUUcdtXyh/17TXoC11ftzM9tXRLv66quzkZJjmH3s2LHFIpYJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoIUFGgqwXnLJJWHgwIHZ6dUKe6YFRo4cGUaPHp0uhgcffDCcccYZ2XJ+ZrfddgsHH3xwWHXVVfOrl5uPj1o/77zzQhyxMz/FkVwnT56crYqB1yeeeCJbrnfmRz/6UVi8ePFyxYsB1rvvvjtMmDBhuXI9eYUAawhljsDaXtCyrQBrq/fnZrev+D765S9/GdZff/1k9SOPPBJOOeWUYhHLBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINDCAg0FWC+99NKw3nrrZac3ZsyY8Nxzz2XLxZligPWee+5JwqfFcvnloUOHhl133TVsvvnmSWCtd+/e+c3J/Pvvvx9i3cUpPyLkO++8E775zW8Wi3R6uRhgnTFjRpg0aVKn62vFHQVYWyfAGvtHq/fnZravFd8f2kSAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECnRdoKMA6ZcqUsOGGG2ZHj4/xjqNM1pr23XffcNBBB2WbOxP63H777cMhhxwS4oiP+emqq64KN9xwQ35VmDZtWujTp0+yLo7Autdee1Vsb2RBgHV5vSFDhlSEeBcuXJiMort8yepr8gHIOKLu+PHjqxfswrWtMgJrPOVW78/NbF8XXnKHIkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEuEGgowJp/5Hls68knnxweffTRms0ePXp0xUipMRx40UUX1Szf1objjz8+fPnLX86KVAs8FkeI3X333bPyjc40K8AaQ5vFUWYfeOCBJLzYaJs7ur8RWCtHYJ07d2445phjOsR46623hl69eiX7xHB3DHnXmvLvp2XLloXhw4dXFG31/tzM9lVAWCBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBHi/QUIB13Lhx4Stf+UqGcO2114b4v1rT2WefHbbddtts8wUXXBB++9vfZssdmenfv3+44oorsl3mzJkTYnvyU/F499xzTzjvvPPyRTo936wAa34U0rRxb7zxRhg1alS62GWvAqwh/OpXvworr7xyYv7aa6+Fb3/72x3yLzPA2ur9uZnt6xC6wgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQ8gINBVjjCJGHH354dpJvv/122H///bPl/MygQYPCT37yk4rRRQ855JCwYMGCfLEQy8V6Fi1aVLG+uPDVr341HH300dnqO+64I0yZMiVbjjMxZPrzn/88rLTSSsn6OKrlYYcdFl555ZWKctUW9tprryS8WG1bXCfAurzMkCFDwqRJk7INCxcuDAcffHC23N5MPrxbbUTd9vZvxvarr746rL322knVH330Udhjjz06dJgyA6yt3p+b2b48+siRI8Nuu+2WXxWuueaaMGPGjIp1FggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgdQUaCrDG05o6dWpYbbXVsjNcvHhxOPXUU8P8+fOzdX/7t38bTjrppIrw6nPPPRfGjBmTlUln4qism266aYjbr7/++hBHTS1On/vc58KZZ55ZUd9ZZ50VZs6cWSwavv/974eddtopWx9DiLHNMZhYnGIAb/To0WHYsGFJ3fGR7tOnTy8WS5YFWJdnWREDrLEPbLbZZtnJvvrqq2HChAlh7ty52bq2ZsoMsMbjtHp/blb78sann356+PznP59flbynr7zyyop1FggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgdQUaDrDusMMOSTi1eIoxKLpkyZIk3JqOgJqWidvi6Kuvv/56uip7/elPfxo23njjbDmWfeedd8Kbb74Z4vy6664b1lprrWx7nGlvpM9p06aFPn36VOzz4YcfhnfffTfE1969e4fVV1+9IhAbCwuw3pKZxNFr44i7bU31BFhj8DeO0tqrV6+KqmIfyV+jeK2XLl1aUSYuPPnkk+GUU05Zbn2zVmy99dZJYLVa/bHvpNOcOXPCiSeemC5mr2UHWGPFrd6fm9G+DPSvMwKseQ3zBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6JkCDQdY42mffPLJ4Utf+lJdAjEIedVVVyUjJlbboRhgrVYmvy6GHGMY9q233sqvrphfZ511wtlnnx0GDx5csb69BQHW8gOscXTbM844oz36mttfeOGFcMQRR9Tc3owNp512WoijCLc11RpRuBkB1lbvz81oX95egDWvYZ4AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQI9U6CUAGs89W222SaMGzcu9OvXr6bE/PnzwznnnBMWLFhQs8wuu+wS9txzzzBo0KBs9M9qheMInf/xH/9Rc3TMavvEeg866KCKkT6L5WK9sZ0zZswIt912W3Fztty/f/9wxRVXZMuxfBxZtNEpHrM4Yu0bb7wRRo0a1WjVHd7/lls6FmD99Kc/HX7+859nx3n55ZfDYYcdli3HmZ4YYI3t3nXXXcOIESPCBhtsEFZeeeXlrtFTTz0VjjvuuFi0YupsgDWO7hqP19bU6v25zPblHaoF5qdOnRquvPLKfDHzBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINDCAqUFWNNzHDhwYNhqq63C5ptvnjwmfsmSJeHpp58Os2bNCosXL06L1fUaH0kf64mhwb59+yahwVdffTUJmD744IN11VGr0Be/+MWknbHeOCrsSy+9FOIomo899litXawn0LICrd6fW719LXthNYwAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIrqEDpAdYV1MlpESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIlCQgwFoSpGoIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTqExBgrc9JKQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgZIEBFhLglQNAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAfQICrPU5KUWAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFCSgABrSZCqIUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqE9AgLU+J6UIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRKEhBgLQlSNQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAvUJCLDW56QUAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBASQICrCVBqoYAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKA+AQHW+pyUIkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKElAgLUkSNUQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjUJyDAWp+TUgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAiUJCLCWBKkaAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB+gQEWOtzUooAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAkAQHWkiBVQ4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUJ+AAGt9TkoRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAiUJCDAWhKkaggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBOoTEGCtz0kpAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBkgQEWEuCVA0BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEB9AgKs9TkpRYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUJKAAGtJkKohQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoT0CAtT4npQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBEoSEGAtCVI1BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC9QkIsNbnpBQBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBJAgKsJUGqhgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoD4BAdb6nJQiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAoSUCAtSRI1RAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNQnIMBan5NSBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECJQkIsJYEqRoCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIH6BARY63NSigABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoCQBAdaSIFVDgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQn4AAa31OShEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECJQkIMBaEqRqCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE6hMQYK3PSSkCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGSBARYS4JUDQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQH0CAqz1OSlFgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQkoAAa0mQqiFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKhPQIC1PielCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEShIQYC0JUjUECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQL1CQiw1uekFAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQEkCAqwlQaqGAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgPgEB1vqclCJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEChJQIC1JEjVECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1CcgwFqfk1IECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIlCQiwlgSpGgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgfoEBFjrc1KKAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgJAEB1pIgVUOAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFCfgABrfU5KESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIlCQgwFoSpGoIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTqExBgrc9JKQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgZIEBFhLglQNAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAfQICrPU5KUWAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFCSgABrSZCqIUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqE9AgLU+p6zUBRdcEFZdddVkefr06WHq1KnZNjMECBAgUL9AV95PJ06cmDXsxhtvDPfdd1+23FUzn/nMZ8IxxxyTHe6iiy4Kf/jDH7LlMmda4XzLPB91ESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIrHgCnQqw7rDDDmHfffctTePCCy8Mc+fOLa2+ZlZ06623hl69eiWHmDdvXhg7dmyHD/e9730vDBgwoGK/GKh6/PHHK9YVFwYNGhS+853vZKuffvrpcPXVV2fLZsoV+MpXvhL22Wef0L9//7D66qtn1/3DDz8MS5YsCc8//3y46667wp133lnugdVG4GMiUMb9tF6q22+/PSsaw6vjx4/PlrtqZscddwwnnHBCdrg77rgjTJkyJVsuc6YVzrfM81EXAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAiifQqQBrDGB+7WtfK01jwoQJ4e677y6tvmZWVEbg6pZbbgm9e/euaOYrr7wSDj300Ip1xYVDDjkk7LXXXtnqevbJCpupW2Dw4MHhBz/4Qfj0pz9d1z7Lli0LP/zhD3tMH67rpD4mhSZPnhxWWWWV5GxnzpwZLr744o/JmZdzmo36lXE/rfdMWiHQKcBa79VSjgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA4OMgIMDawatcRuCqWoA1NiOOzDdr1qyaLRJgrUlT2oaBAweG+FjvYsC4vQPE4GPsG6aeJXDbbbeFlVZaKWn0nDlzwrhx43rWCXRzaxv1K+N+Wi+BAGvXjzhb77VRjgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA4OMp0KkA6/Dhw8OIESNqiq299tqhT58+2fY//elP4d13382WizMTJ04Ms2fPLq5uyeUyAle1AqzPPfdcGDNmTM3zFmCtSVPahquuuiqss846FfXNnTs3/Od//md4/vnnk22bb7552HjjjcOmm26ahR8FWCvIesxCowHMHnOiTWpoo35l3E/rPbUrrrgiC6ZPnz49XHbZZfXuWlq5rhyBtRXOtzQ4FREgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECKyQAp0KsLYncfrpp4fPf/7zWbHrrrsuXHPNNdlyT54pI3BVK8AaXY466qgwf/78qkQCrFVZSlu52267hSOOOCKr76OPPgrHHXdciAHWWtMPfvCDsMMOO4Sf/OQnIYbiTD1LoNEAZs862/Jb26hfGffT8s+qeTV2ZYC1eWehZgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLlCAiwdtCxjMBVWwHWp556KglNVmuWAGs1lfLWnXPOOeFzn/tcVuHPfvazcOedd2bLZlY8gUYDmCueSMfOqFG/Mu6nHWtx95YWYO1ef0cnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoLYGWD7B+5jOfCdttt13Ycsstw4YbbhjiqJgvvvhimDVrVrjnnnvCSy+91GHRgQMHhu233z5sscUWYfDgwWHdddcNS5YsCa+99lqYN29euPTSS2vWWStwNWrUqDBs2LCw1lprJW184YUXwiOPPBKmTZu2XF3FAOvSpUvDKquskpWLQdUFCxZky+lMZwKsX//618PWW28dNtlkk7DaaquFRYsWhWeeeSY89thj4Xe/+11a9XKv22yzTdh8882z9dXOI9uYmxk0aFDF6Lu19hswYECII57GdvXr1y9x+8tf/hJefvnlZMTTxx9/PDz44IO5mps/e/3114fVV189OdCyZcvC8OHDGz7orrvuGvr06ZPU89BDD4VXXnmlZp3RPPrF6cMPPwy/+c1vlis7cuTIbN3TTz8dolPcZ6eddkreJ7EvR8N4fX//+9+H559/Pitfbabs+orH6Gz/K9YTl/NtjaPdLl68OCkW33d77bVXWG+99ZI+/s4774RXX301TJ06NcyePTurKr9/unL06NHpbPL+v+OOO7Ll/Mwf//jHuvrjzjvvHNZcc838ruH1118P9957b8W6Vl+I98gDDzwwxPvYDTfckDS3GX5l3E+rWQ4dOjT079+/2qZk3bPPPhuee+65mtvTDfEekN6bH3jggez91JH7fVpXfO1ogDX26169emVV1Lqftur5Zg3/60z8cUAcrTp+HsXPydi34j0+fg698cYbyX1s0003TXaJ7+H8/T+OjB0/Jy+55JJ8leYJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgR6uEBLB1gnT56cBfqqOceQYQxXXX311dU2V1139NFHh69+9atVt6UrY71PPvlkGDduXLoqey0GriZMmBBiO1deeeWsTH4mhgkPO+yw/KpQDLDefPPNYe+9987KxODrKaecki2nMx0JsMag1HHHHRd69+6d7r7c67vvvhtOOumkMH/+/OW2FY8Vg31TpkxZrlxxxaRJk8KQIUOy1aeeemp4+OGHs+UYgov/K4b8sgK5mRgqju2bO3dubm3zZuN1SMNqMSi9xx57NHywYn8ZO3ZszTrPP//8sNlmmyXbawVob7/99mz/GFJ94oknwv7775+tK87cdNNN4fLLLy+uzpbLri+tuNH+l9aTvn7qU58KF110UboYZsyYEWJfi2HzGFytNcXQ8GmnnZZszp9rrfK11sew45gxY2ptztZXO0YM58XAY0+Y/vf//t/JvWiDDTZImpu/F1U7t3rPqZZf8f3RmftptTYU77HFMvfdd18YP358cfVyy2W3ryMB1l/+8pdh/fXXz9r05z//OXz3u9+t+uOGVj3ftPHnnntuiCHbalO8151++ukh3hvjjxni9Kc//Snst99+WfErr7wy2RbLxuD+ddddVxFwzQqaIUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6FECLRlgjSGfY489tmYotCgcR01tKxgYy8dg4JlnnpmM/Fbcv9bykUcemY24l5bJB5ri6K9xlL++ffumm6u+xlE3Dz300GxbMWx0wgknJEHNOCpdnGJIJ45+GEdLzU/FUGmx3rTsMcccE3bZZZd0sc3XeKxf/OIXIZ5Xfoptueaaa7JVsS0HHHBAtlxrJu/z/vvvV4yaGfeZOHFiMvJtrf2L62P7LrvsshDDpc2e4vmm1yAe6+STTw6PPvpoQ4fNe7TXTzsaYI2+6eiubTVy5syZ4ayzzqpaJB9KLKO+eJAy+l+xscUAaxx9edtttw2f+MQnikUrluOoyt/+9reTdflzrShUx0KtAGZx12rHaPUAa7SN95svfOELy91zuzLA2tn7afEaxOXiPbZYpjMB1jLaV2+ANYbO42jK6fTee++F+Hm0cOHCdFXFa6ueb2xk/Hz55Cc/WdHe4kIccTr+aCD9AUGtAGt+v1jm3//93+v6YUV+P/MECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKtI9ByAdb46Oo48txKK62UKcVgy1NPPZWMFBrDonGEz/RR62mh+GjlGHSsNd14441h1VVXrdj85ptvhvho8BhOi4+032ijjUIMc6WPbG4vwJqvLAblnnnmmWTf+Ijk9FH0aZkY6ktHEi2GjWKANR47Hi+d/vM//zOcc8456WLyWk+AdbvttlsurBhH7osjysYgXTy/GObNj8xaa7TRiy++OKQjMcYGHH744SGGuGpN8XHbsUw6xZDheeedly4mr/kAazxufNT7ggULksdJxxBTPF4MJhaDmUcddVTVkWIrKm9wId+2WFV0iyPhRrvOTs0MsObbFEf6nTNnTuIW+98666yT35wEh++6666KdXGhWuAyru9sfWX2v9iOdCoGWGPfSd+nscwHH3wQ4vs5Bv3iKI5xhN94D8kHWON7MB3hMa039rV0ivvG+0y16fHHHw9Tp06ttqliXTXPVg2wxhGG99xzzxDvubWm/Hu4GX7590e+DR25n+b3S+fjjxXWWGONdDHpK+noxnFlZwKsWWV/nels++oJsF511VUV79+33347HHHEEWHx4sX5JlTMt+r5xlHAd95554q2RrvZs2cn6z772c9WBHXTgsUAa/GzKC0XX+OPHOIo4nE09nvvvTe/yTwBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECLC7RcgDU/CmW0i2GXE088cbnHJscRRo8++ugsxBYDbXEUwWohnxgM/cY3vpFdihh4iSPcxdBrcYojcMbHGW+++eZJoPT555+vKFIMXNWq62c/+1kYPHhwtm983PtJJ52ULFcLsM6aNSsJ4KQjSlYLldYTYC0+djoGRL/zne9k7YgzMbAW25cP9OaDamnhkSNHhtGjR6eLYfr06SFen1rT5MmTK4LF1UKnZ599dmJ75513thk4juXy4cIYIIz9oJnTbrvtlgTFisd44YUXwu9///vw61//umr/KpbPL+f7S9kjsKbHqRbeLj6yO76P0pFI0/3ia7XAZSP1ldn/8u0sBljTbTH0fMEFF4RiODeOXnn88ccnQfJ8MDzdL3297bbbsrB8DACPGzcu3dSp12qerRRgjcH/gw8+OMSgcT7Enj/ZeO+JAcMYCHz44Yfzm5abb9Qv//6IlXfmfrpco2qsyF+bzgZYG21fWwHW+NkzZcqUJHydnsJbb72VfK6lyx15bYXzLX7WxXto/JzIT/FzcYcddsivCsUAa9y46667hhEjRiQ/9sj/wCW/Y/zRQbxXxxBwtDMRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAi0tkBLBViHDRsWzjjjjEwshlH22WefbLk4Ex9p/8///M/Z6jj6Wgzu5acYZLv00kuzoGvcFo/x4IMP5ostNx8DNdVGcysGrmoFoYqBu3yAsBjqiSOwxgDrvvvuGw466KCsLTNmzAiTJk3KltsLsMbAZwx+plMclXKvvfZKFyte4yOd46Od06laYDZuywfU3n333QrvdN/0NV82jhq4//77p5s69XrzzTdnj5Ru61w6VXmNnX7+85+HT3/60zW2/teorHG0vxjyvP/++2uWSzfk+0szAqyx38T+U2269tprK8JwMSgWg9T5KR9yi+sbqa8Z/S9ta/H9FNfHPnHooYfWfKx6um9br/k+uyIHWGMYPY6QnH8sfdEljoQcA4bVgv3Fsulyo37590esszP307Qt7b3m+3qt4xTrKLt9tQKs/fv3T4Kd6Q8YYjvynxnFdtWz3N3nW/zhSFv3vyuvvLJidORqAdb8OcfPwn/4h38IMfRba4o/Pokh7N/97ne1ilhPgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQzQItFWAtPsJ9woQJ4e67726T6Kabbgp9+/ZNylQL/MRRJ/fee++sjpdeeqniMffZhjpnioGmUaNGhTjCYrXpuuuuyx5jHR9PHgOqcaoVYI3bYnisT58+cTbE0SXjiHPp1F6A9fvf/37Yaaed0uKh2qiq2ca/zhQfyxxHsvztb3+bL5KMuJp/9PbYsWNDDCIVp/32269ipMBqI+0V92lvuTiK7e67797eLg1vj4Goc845p2L03FqVxoD1jTfemISkapXJ95e2Alxx//zow3Gkxxg4LE75UFrcdvLJJ4dHH320WCxZPvzwwyvqqNYfyqyvGf0vPbFqAdYYcI7h9EamRgOYxWMXPeP27hqBNb5vYyB+6NChNUdbXbJkSRLUj2HnGGDt6NSoX/79EY/dmftpvW3OX5vOBlgbbV+1AGvsxxdeeGH2ORbP5+WXXw6HHXZYvadWtVx3n298b6633npZ29r64cg3v/nN8K1vfSsr216ANS04ZMiQ5IcSMTxfa0Th999/P/zHf/xHMirr66+/nu7qlQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoAUEWirAGh/7u84662Qsl19+efJI6XRF/rHBvXr1SlbvscceYe21107mi4HPuLL4KPq4XM/ImUmFVf4vH7hqb4TY/OPU821rK8A6evToEEdLTKcYELvooouSxfYCrD/60Y/Clltume4aaoVN0wLFEV9/9atfhUsuuSTdnLzGxzbHetKpWggyboujucZRXdMpPqZ84cKF6WKbr5tssknyWOj1118/e5x73CGGcTfYYINs364IsKYH23PPPZPRf/P9Md1WfH3qqafCcccdV1ydLOf7S9kB1hjMyveVYgNi2+N7Kp2efvrpcOyxx6aLyWs+5NZofc3of2ljiwHW/PspLdOZ10YDmMVjxrDommuuWbH6zTffDLNnz65Y1+yFH/7wh2Grrbaqepg42vKTTz4Zpk6d2u5I1FUryK1s1C///ujs/TTXnDZn8329MwHWMtpXDLDGz6L/9b/+V/ajhXgCzz33XBgzZkyb51LPxu4+3/yPMZYuXVrxQ5Jq7c+3t94Aa76er33tayHetwcNGpRfXTGf/zyt2GCBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgWwRaKsBaDHZ2RqQ4Ql4xWNloCDIfuKo24mu+zbVG1CyeZ3wEfHx0ezrFIOnKK6+cLOaDP+0FWIsj3rV3rl/4whfCqaeemh42zJw5M5x11lnZcjqTb2+tEFc+fNSeS6w3jrgXR5fNPzI7PV6t1wMPPDC89dZbtTY3Zf2AAQOS4FUc4S8GbGuN8lcr2JvvL2UHWGMwMo6w2daUDxjGQHEMFuen/HVrtL5m9b/Y3mKAtdq55M+r3vm8z5w5c8K4cePq3bWlyxUfyR4bG9+Xd955ZxJcLavxjfrl3x/t3Tdq3U/rPZd8X+9MgLWM9hUDrNXaXlbIsrvPN983Fi1aFA444IBqp5uty3/OdCbAmlX015k4+vRXvvKV5cLkjzzySDjllFPyRc0TIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAh0o0BLBVjzgZvOmhx55JHh+eefz3a/6aabskczx5EH44itjUz5wNX8+fPDUUcdVbO6WoGrfFAn7lwMsH73u98NX//617N6r7vuunDNNdeE9gKsHT3XOFLd5MmTs+PUGvnv3HPPTR5DnhY88cQTw+OPP54uhuKosWl7swK5mTja6r/+679WjDiY29zmbDxOdz8CetiwYWH48OFhu+22qxgtdtmyZeGII44IL774YsU55PtL2QHW2M9jf29ryoehq4WP8++5RutrVv+L51cMsLY16m1bHsVt+ZDdih5gjeHvGTNmhDiydVlTo37590dn76f1nku+r3cmwFpG++oJsMbzKX4m1HuO+XLdeb79+/cPV1xxRdacF154Ibk/ZiuqzOTvH40EWAcOHJj8QOLv/u7vwqqrrlpxJAHWCg4LBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFuF2iZAGsxTPnBBx+EJ554osNA8THmixcvzvbLh0Xbe0R6tlMbM/nAVWcDifk2xUNVCyvly6TBw/YCrPl9YqAyBi3bm/Ihp1ojDMawZn5k1gceeCCceeaZWdUxqBQDS3Fq67hrrbVWiKN09unTJ9s3zsTze+ONN0Icpe+9997Ltm255ZZhjTXWyJZbIcCaNiYGcSdOnJiNlBvX//rXv64IBMd1ZfSXWE865a9XPSHOm2++OayyyirJ7h9++GEy6m1aV3wts75m9b/YzmKA9e677w4TJkyImxqaGg1gNnTwJu4c35/bb7991SPE92gMq8e+cdddd1UtU+/KRv3Kfn+01e58X+9MgLWz9/t8m2oFWJcsWVIRtkzv+fl9Ozrfnedb/Dx/9tlnw/e+9702T6HRAOs+++wTvvGNb4T11luv6nFiv4/HyAdrqxa0kgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoMsEWibAGs84H7h55513klHUGpW4/vrrw+qrr55U01a4st7jlBG4ygf94nGrBViPP/748OUvfzlr1i9+8Yuw7rrrhr322itb98orr4RDDz00W+7ouW622WYhjhKbTnPnzg3HHHNMuljxOm3atCx4unTp0rD33nsn2wcMGBAuu+yyrGxbo3jGANPXvva1rGwMFMfjxzBitSkGFLfeeutsUysFWGOjRo4cmYw+mzZw1qxZybVMl+NrR/rLT3/607Dxxhsnu9fqq/n3yEsvvZQ8Kjt/vOJ8vq/FcPC+++5bUaTMzU1TIAAAQABJREFU+prZ/4oB1jiS6KRJkyrOpTMLjQYwO3PMrtonjkR54IEHhi996UvZe7d47PhDgYceeijEUZNjQLOjU6N+HXl/1BrRut425/t6KwVYo+FFF10UbrjhhvCJT3wiO516AupZ4Soz3X2++eMvXLgwHHzwwVVa+T+r8qNF1zsCaxwRe7/99gtbbLFF6NWr1/9UlpuLf0v89re/TX48kVttlgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoAUEWirAmg9JxmBVPqzZWavJkyeHOBpcOsVAV3yUdmenMgJX+VBhbEe1AGtcnw+HxRDO9OnTK0yKAdaLL744bLDBBnHXZNp9993T2aqvMSAbg7LpdM8994TzzjsvXax4/cEPfhD+/u//PlsXR2SdOXNm8gj7OOpdOk2ZMiXccccd6WLF6zXXXBPiKKxxigHNo48+OsTHctea4mit+dH0Wi3AGs8lnlM6VRvBNt9f4qiXY8aMSYsv93rllVeGfv36JevrCbDGkYa/9a1vLVdPfkU+RLZgwYIQR/HNT/ntjdbXzP4nwJq/ah2f33nnncM//dM/VdwLi7XE6////t//C5dccklxU83l/D1qzpw5Ydy4cTXLVtuQf3+UMcJptWOk6/J9vVUCrPl2xBGn//Vf/zWstNJKaZOTHwfEz8XOTN19vvlAarXwfPGc8u1tK8D6yU9+Muy///5JMLtv377FapLljz76KMT+ePXVV4fHH3+8ahkrCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEul+gpQKsxcBiewHMevhOPvnkJOiSlo3HiI/O7uxURuCq3gDr6aefHj7/+c9nTY2PYU5H6IwriwHWc889NwwdOjQrf/bZZ4f7778/Wy7OHH744WH48OHZ6jiCZgz8VJs22WST8JOf/CTbFENBJ554YhLgTEOpMTS0xx57ZGWKM3m7N998Mxx00EHFIhXL8Tqtssoq2bpWC7DGhuVDV8XrEbfnz+GNN94Io0aNiqurTvkAdz0B1g8//DCMGDGial1xZXGE3dmzZ1cElmOZfPsbra+Z/a8rAqxtjUAcreqZxo8fH3r37l1R9IEHHgidDSFWVFTCQv/+/cMBBxwQ4qPsa4X/Yt974YUXQgwgxpEr25ryAdbO+OXvCR/HAGsM+8fQfzrFgHn+hxvxnhpD73Fk645O+fd2PijbVj1lXo/8DxbiMdv68ch2220X4o8i0qlagPWb3/xm+OpXv1rxo4a0fPoaQ9i/+c1vQvwxgIkAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKD1BVoqwBoDl9tuu22m1taIoFmhdmb22WefikcX1zMSXFtVlhHwqTfAOmDAgOSxx/kR+fJtKwYmv/vd74avf/3rWZH2HkEdHxu+xhprZOUnTJgQ7r777my5OJMPJMWw41FHHRXiCLfp1N4IjPmwW7HtaR3pa/G6xfWtFmCNYao4imw6xbBw7MP5KQaC11577WTV+++/H0aOHJnfnM3H0Rd/9KMfZcv1BFhj4bZGvD3llFPCF7/4xazOGTNmhEmTJmXLcSYfcovLjdTXzP7XrABrfpTIaiPoRpOOTEXPuG97weWO1F9m2Rhi3XfffcPgwYMrRv3MHyOGb88888z8qor5Rv3KuJ9WNKiNhfy16Y5AZ2xaNI8jbqdTMcAa1xdHMq5nZOS0vvxrd59v/DzZeuutsybFEcTPP//8bDk/UyxbDLAWf9yS3zeGfJ944okktPrkk0/mN5knQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBocYGWCrDGkNrPf/7zLEwVQ3yHHXZYMtJoe45x1LoYpqo25Ue2jNurhYaK+8Uw3oUXXlhcHcoIXNUbYI0HP++888JnP/vZ5doRVxRDoAMHDqx4/Hf0+z//5/+El156abn9v/zlL1eMxtlWuDLd+cgjjwzf+MY30sXw6quvhvXXXz9bjiNw3nvvvdlyceaGG24In/jEJ5LV7Y32WbxmcadmB1jjaL1bbbVVuOqqq8Kdd95ZbP5yyzfddFPFKJZx1L+pU6dWlIsB30GDBmXrao2Ke8EFF4RNN900K1dvgHXhwoUVAe2sgr/O5MOFcf2hhx663HspH3KLZRqpr5n9r1kB1nzAuL0RhKNPe1PRM5Zv1QBrei5xBOVvfetbYaeddgqrrbZaujp5feSRR0IMQteaGvUr435aq23F9flr08oB1nXWWSdcdtllFSP5zpw5s2KE0uK5VVvu7vMtjtod7/ljx44Nzz33XEVzY8g1fs7lf6hRDLDGe2u/fv0q9oujeP/f//t/Q/whhokAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBnCrRUgDUSfv/730+CVClnDJXFUGC1R9vHUFsMNQ4bNiwJ+8TR3eIob8Vp1113TYIz+fVPP/10OPbYY/OrkvlY1/HHH58EuWJgs/jo5jICVx0JsMbwY36U03yDiwHWuO1f/uVfwjbbbJMV++CDD5LziY/3TqfoEUcOzQeGonF7j11ed911w+WXX55WU/G6dOnSsPfee1esKy789Kc/DRtvvHG2OgaZ4uOx81MMkMaQZ58+ffKrk/lmB1hjAHfo0KHJsd55553kse8x6Pbiiy9WtCWGeEeNGpWFcePG2E9jQHTBggUVZeMotf/4j/+YrVuyZEkYN25cRYirWki53gBrrLia47XXXhvWXHPN7Li1Hu+eD7mlhRupr1n9r1kB1njP2GyzzdJTT0LZcTTI/Psl21jHTDXPVg+w5k/rb//2b0N8VHs0ifeH9gKsjfqVcT/Nt7+t+fy1aeUAazyHXXbZJRxzzDEVpzNx4sRw1113Vaxra6EVzrcY4I8h1jjC7K9//euk6bvttls4/PDDQ69evSpOpVaANe7/2GOPJZ9V8+bNq9jHAgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQM8TaLkAaySsNvpmDK68++67Ib727t07rL766hUj1MX9agVY47b4aPQNN9wwzlZM7733Xvjzn/+chLXiSIT5IE0rBFhjY4shsfQEqgVYi6NgpmXjeS5atCj0799/uXBoPaOvpvXUepRze48aj/vHcPAZZ5yRVpW8xusZR3Lt27dvEhoujgCZL9yVAdb8cWOYNAaqYt+I7cz3kbRcHDEx9ttq02233VYRFo71xYBsfI0h03yQON2/IwHWuE8M0L799tvhb/7mb5L3RrHOGIarFsrMh9zSYzdSX7P6X7MCrHH0xxhYrTbFvplOc+bMCSeeeGK6WPO1mmdPCrDmTyyOfj1//vyqPwpIyzXqV3aANfaTSZMmLfceje+HfCg+vl9i6L44xUfQ50ecLbt9O+64YzjhhBOyw7Y1GviZZ54Ztt9++6xs7I/f+c53klGS05Wtfr5bbrll+OEPf7jc9UjbX+u1GGCNI7e+8MILNe+xteqxngABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoLUFWjLAGh+hHEfhHDx4cIf02gqwxopOPfXU8IUvfKHuOlslwDpkyJAklFVseLUAayzz93//98lItjHo294UQ8EnnXRSElRrr2zcvv/++yf/K5aNI+fG8Fd7UzzWDjvs0F6xJFwWQ4Of+9znsrLdFWDNGlBjpr3w7g9+8IPkmtTYPVn9zDPPJIHWdDTQegKsMfgbR8Vt6zrHeuIoiPFR29WmfOCyjPriMZrR/5oVYI3tPe2000IcebStqdqotNXK5z3T7T01wJq2v73XRvzKDohWC8m31/789hiSPOKII7JVZbevIwHW2Ijrr78+CaSnDXrttdfCt7/97XSx6o8Cso11zDT7fGMT4udXDImvssoqNVsU31/xByTxsz9OxQBrzR1tIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6NECTQmwHn/88eHLX/5yBnPdddeFa665Jluud2bPPfcMBx10UMXIecV940h6cZTAGTNmhDjSZXtTHNEuhi3XWGONmkXffPPNcMstt4Sbb755uTKdDTTF0fNGjBiR1PerX/0qrLzyylndY8aMCTHA09ZUbQTZWgHWWE8caTU+mv6Tn/xk1WpjsPGJJ56oa1TJYgXFEUU7GjbaZ599wgEHHFAzePnss88moyCOGjUq/OM//mN2+N133z2bb9bMyJEjw8477xwGDRrU7qiBMfx1wQUX1BXcjY/JHj58eNVmx/571FFHVYy0m+8v+Z3yAcn4KO0YcDv99NOrvkfiyMJxFMfHH388X0XFfNn1pZWX3f9ifVdccUVaffJ+jyNtljXtuuuuyftzgw02SN6bxRFsn3rqqXDccce1e7jieyPusKIHWOM5dtavjPtpPH46tUqAtdb794tf/GLFCK9tjcAazymGP3/84x9XjNJ85ZVXhqlTpyan3Ornm16XGLSP75+NN944C+RGoxiav//++0McwTqeUzoCdzGom9bjlQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYMUSaEqAtRlEMfiz1VZbJY9wj+HLl156KQl9xhBfZ6c4uudnP/vZsNFGG4VFixYl9cVQ5/PPP9/ZKltyvxjaHTp0aGK3ePHi8PDDD4c4uml3T/GaxkdMx9E133nnnSTM9Nvf/ja89dZb3d205PjbbLNN2HDDDZMQ8IABA8L777+f9I0YNp43b16Ilh2Z1l577WTExNiPP/jgg+Scf//733eovxUDp3FE2zgNHDgw7LLLLkkwO4a/7r333rBgwYJ2m1d2fdUO2Kr9r1pbrSNAoPkC8QcCxc/ZfJh51qxZ4YQTTmh+QxyBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgWwV6TIC1W5UcnECLCNQKnHa2eWXX19l22I8AgY+vQPwRw0UXXZQBxB8yxNGtTQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAiu2gADrin19nd0KJlB24LTs+lYwbqdDgEAXCPzLv/xLiCNep9OkSZPCjBkz0kWvBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECK6iAAOsKemGd1oopUHbgtOz6Vkx1Z0WAQGcFpk2bFubNmxeuv/768PDDDy9XzRFHHBF22223bP2iRYvCAQcckC2bIUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQWHEFBFhX3GvrzFZAgbIDp2XXtwKSOyUCBBoQyN9j3n///fDmm2+GhQsXhjXWWCOsv/76oW/fvhW1n3XWWWHmzJkV6ywQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAismAICrCvmdXVWK6hAPgz22GOPhZNOOqmhMy27voYaY2cCBFY4gfw9pq2TW7ZsWfj1r38dpkyZ0lYx2wgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBFYgAQHWFehiOpUVXyAfBvvDH/4QTj/99IZOuuz6GmqMnQkQWOEEJk+eHDbYYIPQu3fvmucWR2U9++yzw9y5c2uWsYEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGDFExBgXfGuqTNagQWGDRsWevXqlZxhGY/ZLru+FZjeqREg0IDAJptsEoYMGRIGDBgQ+vXrFxYuXBhmzZoVHn300QZqtSsBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBPFhBg7clXT9sJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAj1QQIC1B140TSZAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9GQBAdaefPW0nQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQAwUEWHvgRdNkAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBPFhBg7clXT9sJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAj1QQIC1B140TSZAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9GSBlguwXnDBBWHVVVdNTKdPnx6mTp3ak321vYcJ6H/Nu2AXX3xxVvnll18e7r333mzZDAECzRU4+eSTw0YbbZQc5Mknnww//vGPm3tAtfdoge7oLz5/e3SX0fgWEuiO928Lnb6mEGiqwMSJE7P6b7zxxnDfffdly52d2W233cJWW20VPvWpT4XVVlutoprjjz8+LFq0qGJdWwvNaF9bx7ONAAECXSng+0JXajtWRwR8/nZES1n9RR9Y0QTK/D7TCjb+3miFq7BitKHZ9/tW//e/Vm/fitDLNtlkkzB+/Pg2T2XKlCnh7rvvbrOMjT1fwPut51/DRs/A3y+NCtq/lQQ6FWDdYYcdwr777lvaeVx44YVh7ty5SX233npr6NWrVzI/b968MHbs2NKOoyIC7Qnof+0JdW77wIEDwyWXXJLtPGPGjDBp0qRs2QwBAs0VuPLKK0O/fv2Sg/zpT38K++23X3MPqPYeLdAd/cXnb4/uMhrfQgLd8f5todPXFAJNFbj99tuz+mN4tb3/UJAVrjKz9dZbh5NOOimstdZaVbb+16rvf//7If7wqN6pzPbVe0zlCBAg0FUCvi90lbTjdFTA529HxT7e5fWXj/f1X5HOvhnfZ1rBx98brXAVVow2NPt+3+r//tfq7VsRelm8D0+YMKHNU7nuuuvCNddc02aZj+vG+G9un/70pzt8+g899FCI/buVJu+3Vroa3dMWf790j7ujNkegUwHW733ve+FrX/taaS2KH7DpL0C8wUpjVVEnBPS/TqDVscvHJcA6efLksMoqqyQiM2fODPlRZ+tgUqTFBXry9fUFpsU7V4s1rzv6i8/fFusETW5OT76fNpmm4eq74/3bcKNVQKCHCJT5H6CmTZsW+vTp0+aZC7C2yWPjCijg74PGLmqr+zXavp72faHR822sN9i7KwXK/PugK9vtWN0joL90j7ujli/QjO8zZbSy0c/fnvb3Rhlm6miOQLPv963+73+t0L5G7wfN6Rnl1SrA2pjlTTfdFPr27dvhSp5++ulw7LHHdni/Zu7QCu+3Zp5fK9bdavcXf7+0Yi/Rps4KCLB2Vs5+K6SAG3xzLuvHJcB62223hZVWWilBnDNnThg3blxzQNXaLQI9+fr6AtMtXabHHrQ7+ovP3x7bXTrV8J58P+3UCXfhTt3x/u3C03MoAt0qUNZ/gDr88MPD8OHDs3P54IMPwv333x9efvnl8OGHH2brr7322my+npmy2lfPsZQh0AwBfx80ptrqfo22r6d9X2j0fBvrDfbuSgGfv12p3fOPpb/0/GvoDEJo1veZMmwb/fztaX9vlGGmjuYINPt+3+r//tcK7Wv0ftCcnlFurQMGDKio8LOf/Ww47rjjsnVGYM0olpsRYF2OxIoOCLTa/cXfLx24eIq2vECnAqzxP7aMGDGi5smtvfbaFaOJxMcVv/vuuzXLT5w4McyePTvZ7g1Wk8mGLhDQ/5qDHB+N+eMf/zirPH55+9WvfpUtrygzrfYHy4ri2irn0ZOvbyv8g0GrXEftaF+gO/qLz9/2r8uKVKIn309b/Tp0x/u31U20j0BZAldccUXo3bt3Ut306dPDZZdd1qmqf/azn4XBgwdn+x5yyCFhwYIF2XJnZ8pqX2ePbz8CjQr4+6AxwVb3a7R9Pe37QqPn21hvsHdXCvj87Urtnn8s/aXnX0NnEEKzvs+UYdvo529P+3ujDDN1NEeg2ff7Vv/3v1ZoX6P3g+b0jObWut1224WzzjorO4gAa0ax3Mx5550XigHgddZZJ3vKatxh6dKl4a233qrY98EHHwxTpkypWNfdC63wfutug64+fqvdX/z90tU9wPGaKdCpAGt7DTr99NPD5z//+axYRz4gvcEyNjPdIKD/dQP6CnTIVvuDZQWibYlT6cnX1xeYluhCPaYR3dFffP72mO5RSkN78v20FIAmVtId798mno6qCayQAtdcc02IP/CL0+LFi8O3vvWtFfI8nRSBjgr4+6CjYpXlW92v0fb1tO8LjZ5v5dW1RIAAAQIEWkeglb/PNPr529P+3midXqElXS3Q6v/+1wrta/R+0NXXtIzjCbA2pnjOOeeEz33uc1kl8Yfr06ZNy5ZbdaYV3m+tatOsdrXa/cXfL8260urtDgEB1u5Qd8yWFXCDb9lL0yMa1mp/sPQItB7UyJ58fX2B6UEdrQWa2h39xedvC1z4LmxCT76fdiFTpw7VHe/fTjXUTgQ+xgLxSRQrr7xyIvDSSy8lj+D8GHM4dQKZgL8PMopOzbS6X6Pt62nfFxo93051AjsRIECAAIEuEGjl7zONfv72tL83uuByO0SLCrT6v/+1QvsavR+06KVvs1kCrG3ytLtRgLVdIgX+W6DV7i/+ftE1VySBHhNgHTVqVBg2bFgyUslHH30UXnjhhfDII490+JcPn/nMZ0L8AN9yyy3DhhtuGGJdL774Ypg1a1a45557QvwPSF057brrrqFPnz7JIR966KHwyiuv1Dz8NttsEwYNGpRs//DDD8NvfvObmmXTDfvvv3/YYostkmHQ11xzzfCXv/wlvP3222HRokUhDnP+b//2b2nRul7L9Bs5cmR2zPgIxjj6TJzidd5rr73CeuutF1ZbbbXwzjvvhFdffTVMnTo1zJ49O9unGTO1bvCN9r84DP1uu+0WNtlkk9CvX7+kH8dr8fLLL4e5c+eGxx9/PLkeHT2nsq9vR49frfyQIUPC0KFDq23K1uWvd7ayykwrvz/y/Tdt+ujRo9PZ8Nprr4U77rgjW87P/PGPf+zU9c7X0dH5+KuxHXbYIWy99dZJ/4v30HgP+N3vfhfeeOON5N6y6aabJtXG91zc1taUP//89Wzk/Vvm/SW2vZH68ueXOpR9fRtpX9qm9DW+5770pS+FrbbaKvTv3z/5XHvggQfCv//7vyfXtyv/wSB+TqWjsL/33nvhvvvuCwcccEDYdtttQ/zs+sMf/pB8di9cuDB88pOfTO738XO5V69e4emnnw5XX3110v703Gq9lulX6xidWV/W/X748OHZ40ritXz++eeT5jT6eRQraaX+khr7/E0lOvfazP4SW/T1r389+fyIf8fEv83i35HPPPNMeOyxx5LPkbZa3RX307aO39a2Zt+vyrof5M+hGe9f99O1ku+E7X2/3HzzzUP8PpZO8fOt3u+O+ffBo48+GubNm5dW0/TXRr8v5NseP6fj95b43tlpp52S79Xrrrtu8p0m3g9+//vfZ59X9Z5YM/rfwIEDw/bbb598Dx48eHCIbVyyZEny93m0v/TSS9ttXnyvxb+pak3PPvtseO6552ptztbn78/pyvhZHv/uiVP8DnzzzTenm7LXP//5zzW/S8RCZbUvO2Bupgy/XHWlzeb7Yit+/1h77bXD3/3d3yXvi4022igsWLAg3H///SH+jROnuD3+PZxOd911VzqbvTb7+2/Z77dG7i/565kClP19K623o69ddb9v5Hq0sl/0bkb7Wvn7QjPOt6P9tiPl8+11Pw3J33f1/nt7Mz5/W/16xL7VyP2+I32z3rLN/rws63zL6i/5vyfL/vehsv+9uN5r2JFyjfx7RDxOM/1i/Y18nsf9mz2V6Ze2tYzvM2ldjbzm759pPY3+PdmsvzfS9rVaf8kblv19P193WX9vdLY/x3/LiX/jp1O9Iyrm/90w7ltrv7Lu92n74muss+z/3lNm/2tG+/Ln39H5fH9L9230fpDWk752tv+l+xdfy64v1l9mgLWsv4eK593Ky60aYG3G+60Z/a+Ra9vsv9caOd+uuL80Yhf3bdbfL43+961mf/6mbmV+vqV1lvnaaPta+f1RplNaV8sHWCdMmBAmT56cjVCSNjx9jQHAww47LF1s8zXWk/6DVLWCy5YtCzfccEMSnqm2vRnrat1Qqh3r/PPPD5tttlmyKbY1dtZaU/wSOWLEiJpu+f1icPe8887Lr6o6X6bfpz71qXDRRRdlx5kxY0aYNGlS8h8TY3C11hRDvqeddlqtzQ2vL16PRvtf/FCL/4vh4fam+B9VTzrppCTQ2l7ZZlzf9o5Z7/axY8eG+A+JbU3xC2vsz+1NxesR6641dfX74/bbb6/VlHbXx//YPWbMmHbLlVXg3HPPrRkqjveS008/PUTbGK6O05/+9Kew33771Tx8M96/Zd5fYsMbra/Z17fR9uUvTvFLVX5bvL5x+5FHHln39c3v35n5+A8D6R/0MbAa721rrLFGRVUxJB3fAxdffHH2I460wNKlS8N3v/vdNn/QUaZfetxGX8u+3xfvf41+HqXn12r9JW1X2edb9vVI29mqn79l+6Xnu+OOO4bjjjsu9O7dO1213Ou7776b/P0yf/785bbFFc2+n1Y9aJ0rm3W/alb/a8b71/10+c5S6/vlnnvuGQ499NBsh/gDyBNOOCFbrjUTHw//zW9+M9sc/6PzmWeemS03a6as+1X+PRxDqk888UQSYqjV7ptuuilcfvnltTZXrG9G/zv66KPDV7/61YrjFBfi30dPPvlkGDduXHFTtnzLLbe0ee+LAebx48dn5WvN5O/PtcrUWn/ssccmP+6ptr2s9hXrLsuvWG+jy63+/WOPPfZI7g8rrbRS1VP9xS9+kfyHpBisTqfdd989nc1e8/0lhq3L+v4bD1Dm+62M+0v+3pIB1DnT7O/TXXG/b/R6tLJfvIzNaF/x/dHo96My/15rxvnW+XbocDH30+XJOvLviWV//rb69Sjjfr+8eONriveDsj4vyz7fsvpL8Xwbvf+lV6Dsfy9O6y3rtYx/j4htaZZfrLvRz/NYR7OmZvh1tK1tfZ/paF3Vyjfj8/fj1l/yhmV+3y/7863R/nzIIYckg2ik/SgOPDNlypR0seZr/G/WccCedDr11FPDww8/nC5mr2Xd79MKW/3f/5rRvvTcO/ua78sdraO975eN9r9ie8quL19/GQHWsv8eyrev1eeLffuyyy6rGVzvqnMptil/3M78999m9r982zo636zP3zLO9/+zdybQVhTX3i8IAoqIgAOgAqIYFUSNvGhYxk+fRF8gKKDBxAmDEyBREBHBAQWfikpwQFCiIjgwGIgTxERYJmJYghijOKE4oagog6hxAKOfu03V26dOd5/url19+9z7r7Xu7erq7updv129q7prnyqf9iUtp6jzpflJfS/x3f4SjyL3x6Xkk9avrkcSz4fOS3JbaAdWmtGGZj1p3LhxbJlp1lI+mGifTPDpZUkv02cft/crDRTY57vs2xVO4oPLgAEDVN++fROLlaRzJs3PfoEhJ1qajaRJkyaxctOsllQ+X4HrQ6L+TZgwIZj5J6m81NmgDlHYLDw6D2n96nyltlSHKzmwaoflSvfk+qj0XCb94CzFrxo6LMSXBktplsu4QE6GNBt1w4YNg9PSOrC6PL/S9lkqP1/6lZJP6/OOO+5QNEtWXCC7QjM+awe0SvqNyyvJMe4QluT8sHOinIKk+YXdO2uatL3n9k+iPaJyFbG+aN7S5ZXWB8kp1X7oMktupfmRbMOGDVNHHnlkIjHJzlB7Q3LYwZc9te+TZd+XvfJR/6SfX9jTbO+XfFCC+k7ktFYp8FnQ6VzqK1O/1meQtFf8Gf7qq6/KfngSVo5ly5apsWPHhh0K0nzUP/qhJzkGN2vWLPK+9gH6gY+e4dw+xnVtH6P9PBxYL7jggsDRNuz+UvLpvKX56XyltpLfD6Tr329/+1t19NFHVywq9cH59468HFilyytlX7htqQjPOqHSNyzr9Ey7/BmTtPdS+ig6Px/ySfd3JftrPsqbqeImuAj2tBxS0u+JdCW3DeU5Je8f6GuLrA8pe6/LKrnl9qBo34t5OaXqCy+v1PchH9+Ledld41LfI0gOH/yk2nNXTlHX++IXdb+o9Lj3mahr0qT7aH/rWn3hDKXe90mHku2bRH2m7wT33nuvqV60shStGFcp8PpAfMihJyxI2XvKu+jf/6TlC+OZJY3X5bTXx71fStQ/Lo90fjxvirs6sBa5/2eX1ce+7Sxa0w6s0s+b7/rnohNub6X6u1Ll9WVfXHjZ10rzk/pe4rP9LXp/XFI+af1S/ZF6Puy6KLFfaAdWXkByXKSlSmmpPVoCe9ttt+WHA8i0FLsdyLHn9ttvV3wmDPr4vHLlSkUzRZFzLP2Cyp6ZlZYCoIbJd+AVTuqDi21IaVlCWrrugw8+CMpLy9nttttuqk2bNkHx4jpnvvjZLzCkE72MIgm1ZcsWtWHDBkXLT9OskDSDKekwTwdWrvus9Y8beCoj6YCWEqQlSslpkHRAjruNGjXit1PnnntuUD9LEv+zI6nfsPxd0/bee29FM03xQI6R9NzqUJMOrFL8yLDrGUt1ufiSkFR3yc6EBVp6dc6cOWGHRNNo1rwjjjiiJE+qyy+99FKQ1rlz52BJ1ZITvtup5OAo9fxK2xfJ/HzoV1I+0tmFF16oDjvssBL10axxL7/8cmBTaGr+MCeOSvotyTDDju0QRrbuz3/+cyDLoYceWpYjzURHP0ShJRz0D03CPghJ8ysTxDFB2t7z/gEXLWt7VNT6ossmXV5pfZCcUu2HLrPkVpqf/bGLZKXlrGnGwvXr1wcfosnZSTvG0/Eo5w4f9pTuJxF82Svp+if9/MKefl97stjTyy+/XHXt2tVUP5pplGYcjQq77rqruvXWW83hjRs3qlNOOcXs+4pI2is7Ly0z73NQP7958+b6ULCl5yBsiXRf9e/+++9XW2+9dYkM9D759ttvK3rfpSWP6D2Y+rH6vTPOgZWcYfkM8nSNXg2FbpLUgfWiiy4qcVqkaw844ADzfYL6PLpvTsd4uOmmm9RHH33Ek0xcSj6doTQ/na/UtqjvH2QPyC7wQO0lfZui7R577FH2zqjPzcOB1cfzZtuErN+bitw/IB35sPeS+ig6Px/ySfd3JftrPsqrbYX0Fva0nGgaB1bp9reo+iBKUva+nLh7CrcHRRxP0SWUqi+8vDpv2mZ5n6HrfH0vprwlguT3CJJHmp9key7By85Dmp+v9xlb7iz7PtrfulZfbFuv9ZD1fV9fL9W+SdZnWglOj4mTnGeffbYiJ6moQCug0jk6xK1kKmXvi/79T1o+zVZi68MeSNY/KqN0fmHc7HvMnDmzxHk77BqeZtuErO/7PM9qihfJgVX6ebPrBukl63iPD51Kt7+S5fVhX6QZSvOT/F7io/0ten9cWj5p/Uo+H9J1mfIrvAMrzehEA4PkUMrDpEmTVPv27U0SLW9Ay6/bgX+EomP0Yj9q1KjAiZCfSzNM0XJ5ejCLBuFpcJEaZ5+BVziJDy70q7Ff/epXRmRylKPyhgV6eGjmWhpIveWWW8JOCZZ55wN1UvzsFxh9c3J0uvHGG8sGO3fYYYfAWYscl2mg0Vfg+qB7uNa/cePGqb322ks9+uijsQ7RdB53fozSm7R+fXG086W6Rr8U0qGmHFh983v44YfNYDQ5EcYtS6pZ+Nzav/xcsGBBMJU6vyfZzW7duvGk1A6s+uK0z6+0fZbOT5dLb131Ky2frV9yPJg+fboWN9heffXVihxZecjbgfXuu+9Ws2fPDkTgnV5KICdv+nBOoU+fPoqWE9CB4uTwr4M0P52v1Fba3ku3R0WtL5q/dHml9eG7/dAcsm6l+dGPv1q1amXEoR/gnHHGGWafItS2U3+cO4zFfczlF7vaU56XS9x2YJWyV9L1T/r5hT3N/n7ZoUMHRU6FOtC7Ec1OEBVGjhyp6Ne2OsyaNUvdc889etfLVtpe2R+vSeiwH3vaS5BGsfFR/+j9sEePHoZn1DscnUA/7hkzZkzwjhbnwGoyYxHOIqkDK7vcRP/4xz+aH+28++67auDAgeaYSySrfHnxcymb1PcD6fpHDurkqK4Dzehrf6+w+epz83BglS6vtH3RLPS2KP0DkseHvZfWh+amt0Xip2XiW1f5pPu70v01XlaKu5bXzk9qH/a0nCR/NqkPQQ4raULW9pfuUVR9+Lb3afiGncvtQRHHU8Jk1mlZ6gsvL+UT1ddNOl5mv19KfS/WZXTdSn+PkObHbQaVVWq8zJWbvl6an86Xb329z/B7ZI27tr91rb5wm6SZu7zv6zyk2jfJ+kyzp9J3QB0WLlwYjH/rfXtrL0kcN+mQfS3tc7ZJvx/Y9tl1vEfaXknLF8ZNMs3VHkjWPyqXdH5hrGynpDQOrEXv/4WVVzqtSA6s0s9bHvXPRR/S7a/v8rraFxdWYddK85P8XuKj/ZVu38KYuqRJyyetX9/Phws7urbwDqxRHTu7Axw2QGbPhkG/JDj++OMjmdmN85IlSxQNxPkMvMJJfHChpRqpg6IDOdCRI12W4JOfrT+Sj2ZdJYfadevWZRFX5BquD8rQpf6lFWjevHlmCXdiQc5cdpDUr523z/2iOLD65lekDos9UBpnX+ylbSs5OEo8v9L2RTq/sOfBRb/S8tn6pZm76Bd5YeG+++4LZrHWxyrpV5+XdWs7hJFdpxlWKZx66qmqX79+Jmv+Am3bicsuu0z94x//CM6V5mcEqKFIEnsv2R4Vub5oFUiWV+eZdJtEH77bj6SyRp0nyY9+UEMvqDpE9UnoeOvWrRUtPahD1Cys+rjeuthTnYfE1oe9SitXpfon/fzCnrr37+1+k/2DC14HaOBHr7SQxSGC55U0Lm2v+KALyfDiiy8qcswNC3afg34oRT801cFH/aMfOt55553mh6h0L5o5cfny5fq2oVv6ARe976cJnEXUe2KS/HwN+GaRL09+SdhEnVPE9w9ayWfixIlG5LAVBPRB+0dclO7bgdXH8yZtXzQfvS1K/0DLI2nvfehDy6m3ReOn5dJbV/kk+7tapqTbSv21sHxcyxuWp0Qa7Gk5RT64lKW/lqX91VIUUR8km297r8ufdcvtQdz3Tso/iX7zLG+W+sLLS2WK6ofa9SlsvMx+v4zjZ7eDvr8nUtl8fI+Q5JdHe04csgYf/MJk8fU+E3avtGmu7W9dqi/Eltsk2nd536frdbDtEaWnHf/1UZ95/fjss89KJoPSsustP/eTTz5RJ554oj6UaMvZRtltnpFtn13He6TtlbR8vOy+4lyHaScckq5/0vlFMXNxYM2zPxQlf02nF8WBVfp5y6v+uehPsv3No7wu9sWFU9S1kvyi7hGVnuR7Cefl2v5Kt29R5cqa7kM+Sf3m8XxkZaevK7wDa//+/YOlSrXAfEuOL3pZP1qumzvF0Hn2IMH48ePV4sWLeRZlcVoCsnHjxkF62Et+2QWOCbzCxX0woNsk+eBiL3NGRoMG9LIEn/zCXmBcZM1SvrBruD7ouEv9C8s/Ls3+lXTYgJakfuNkkT5mO6bV1AysvvnxBjjtC5E0c3rud9ppJ5Nt3ED+r3/9a3XSSSeZcyt9kJR4fqXti3R+BgaLuOhXWj5bv9zZk4kcRGlmuL59+5rkSvo1J2aM2A5h3JbRbOe03IIO9CMR7jzCP+wQM730sDQ/ff+a2iax95LtUZHri9aBZHl1nkm3SfThu/1IKmvUeZL8LrjgAnX44YebW1WaVdVeAoRm0n/sscfM9WERF3sall/WNB/2Kq0sleqf9PMLe+rev7f7TX/961/V9ddfX6Z6cpDkK4Tk1TeUtle8baZCXnzxxeq5554rKy8l0PJ6fNYy2374qH92P4eWAOTL/IUKmjGRs0gyABV1G18Dvlnky5NfFI8k6UV8/7DbyyeeeEJde+21ocU56KCDFC0ryQPvI+t03p67fh/y8bxJ2xddbr0tSv9AyyNp733oQ8upt0Xjp+XSW1f5+PNBeRbte50up966llfnI72FPS0nmuR7e/lV/5eSpf3VVxdRHySbb3uvy591y+2Ba3uZd3mz1BdeXpLXxf7Z75ek66gfftntoO/viVQ2u39lv0/QOTwk+R4hyS+P9pyXL23cB78wGXy9z4TdK22aa/tbl+oLseU2ifZd3vfpeh0k2jcf9Zm3+STr0KFDFbUjdjjhhBOCFVp1ethM1fpY1JazTfL9wLbPruM90vZKWr4obpLpLvZAuv5J5xfFycWBtej9v6gyS6YXxYFV+nnLq/656EKy/c2jvC72xYVT1LWS/KLuEZVeaXyLrpNsf6Xbt6hyZU33IZ+kfvN4PrKy09cV2oG10oypfHpbWrq6d+/eulzBlpYBbd68uUm76667giVWdEK9evV01MzYcswxx6jtt98+SA/L01wgFOEVTuKDC33A+OUvf2mko1lAqMHVs9iZAwkiPvnZLzB5sE5QZMX14Vr/ou5HS9G1a9cuWJqX10FyFmnTpo25LGxAS1K/5kY5RIriwOqbX5E6LHzGr82bN5c4MIapnL9QV/ogKfH8StsX6fzCGLnoV1o+/mMLaf2GlT1NGncIs2djtJ157JnZOGOa1fHBBx8Mbi3NL015XM51sfeS7VGR64vmK1lenae9ddGH7/bDljXtviQ/csTbe++9jQhRH3P1CfQDMppdWQcayLjjjjv0buiWP+t5OfWFCeLDXoXdh9Ky1j/p5xf2NH5Fjkrvl1q//JmLmnXR/lgRN/ig85XYStsr3keMKquWm969qY7p8Oqrr6rzzz9f7wbHpN/PacZo+uWwDrS/dOlSvSu65SySDEBF3dzXgG8W+fLkF8UjSXoR3z+uu+46tc8++xjxKy0pyd/P6KKw931uW1y/D/mw99L2xcD7T6Qo/QMuF9dJlA1MYu996IPLSfEi8uMyusrHdVHE73W8rEXWB+ypranSwbSanoE1y/dxH/bFt70v10K6FG4PXNtLunOe5c3SX+PldbV/vD9StO+JpAsf3yMk+fl43qjcUsEHvzDZfL3PhN0rbVqR+htFry/EltukqL6u1kGl9319Hm0l+hs+6nP37t0Dp1Uta5STPI1L0KpTOpx22mmpVw7lbJN8Pyj69z9p+TRbn1sXeyBd/6Tzi+Lm4sCaZ38oSv6aTi+KA6v085ZX/XPRn2R/LY/yutgXF05R10ryi7pH1vEtyk+y/S16/8qHfJL6zeP5iKpDSdML7cBaaQZU7q0d9oHpgQceUA0aNEjKIvQ8arDXr18fekwikVc4iQ8ubdu2VbfccovijpEk5xdffKHefPNN9eyzz6qnn3469Fdldnl88rNfYNatW6foJaCmA9eHa/3jZaFfLJODdZMmTXhybPyUU05RGzduLDlHUr8lGXveKYoDq29+ReqwcFk+/vhjdfLJJ8dqmT/vaR1Yszy//H6xgsUc5PZZOr+w23KmaR2upOXjsiTRL7dtlfQbVvY0adwhzB586dKli6JZV3WwHeO4nLNmzVL33HNPcKo0P31/H1spe89ZuLZHRa4vWgeS5dV50lZKH77bDy5zlrgkP/sXvmEONlzGgw8+WF166aUmadmyZcGSkyYhJMLrZFp7GpJd5iQf9ooLI1H/OCsJew97+qGiGSejQqX3S33d1Vdfrfbbbz+9G7RtfEZxOsB1F7ZaiLlYOCJtr/igy4YNG0oc1sNE5+W2+4g+6p89sFTJZoXJnDSNs0gyABWVr68B3yzy5ckvikeSdInvB9L1L217aS/DG1ZXeXvu+n1IurykJ2n7Yuue24+a7B9wuaTsvQ99cDkpXkR+XEZX+fjz4fp+xOWS6K/x/HTctbw6H+kt7Gk50aT9v/Irv0/J0v7qvIqoD5LNt73X5c+65fbAtb3Mu7xZ6gsvr6v947Ypyfslb798f08kXaTtXyX5HiHJj/PIWn/59+yseURd54Nf2L18vc+E3SttGq/jWfqTdam+EFtuk1zf97muJNo3X/WZP8dRPwrgXCrZXV5uHud5JPl+wOtuEvvM62qYfebl5HKliXN7JS1fGjmynstlTmsPpOufdH5RTFwcWIve/4sqs2R6URxYed2VsAd51T8XXXCbVsnuVnp/y6O8XEdp7YsLp6hrJfnxe0h+L+Htkkv7y/PhsqaJ8/YtzXVJzvUhn6R+83g+knCKO6fQDqxvvPGGolksokIlA8U7iFF5VEofPHiwWr16daXTMh/nFU7igwsJQjL36NEjVqYtW7aol156KZiy+aOPPgo91yc/+wVm5cqVavjw4aFy5JnI9eFa/0hu+jUCzczSqFGj1MUgp4ow3UjpN7VADhcUxYGViuCTX1E6LC1btlTTp083GnvnnXfUoEGDzH5YhP+iK+yFl18j8fxK2xfp/Hh5ddxFv5LyNWvWTN17771aLPXuu++qgQMHmv2wCJ9hoZJ+w65Pk8Ydwqit6dOnj7mcZnWkX/foYLexvGNHdZJmTqcgyS/I0MM/aXsv1R4Vvb5oVUiVV+cnrQ/K12f7oeXOupXkx9sDexblMPno49bkyZPNobfeeksNGTLE7IdFXOxpWH5Z03zYK5JFqv75eH5hT93eL3Vd69Spkxo/frzeVfZMoxLLzZnMM0Qk7RWvM/ReTHnHBT6YaX+M4nnF5RF3zO47pLVZcXlXOsblTzIAFZUfZ5SkHxeVj52eRb48+dnyptkv4vvHnDlz1DbbbBMUI0l7SW0ltZk6+HZg5fVB3zPt1n7e6HpJ+2LLU5T+AZdLyt770geXtYj8JOWT7O+SXFL9NV5GHi+qPmBPuZa+j1caXyi/ojSFP99p+wdF1IcunU97r++RdcvtQdHGUyqVKUt94eV1Ga/w/b24UtmTHE/bN03yPUKKH8nP9ZekPGHnhPWvws7LkuaDX5gcvt5nwu6VNs21/a1L9YXY8jrt+r7PdSXRvvmqzzS5Bk2yocOoUaPUihUr9K7i3wopcebMmSVjMebEChHOtlL/oOjf/3zIVwGfyGEXeyBd/6TziwLk4sBKeRa5/xdVZsn0Ijiw+nje8qp/LrqQbH/zKK+LfXHhFHWtJD+6h4/vJVLtL29fo3hUSvfZH/chn6R+83g+KvGvdLzQDqwuHyDsl1dyonnhhRcq8Sg7To42mzZtKkuXSuAVzqW8tjw9e/YMZjTdeuut7UMl+zQ73jXXXKOoA82Db372C8zixYtLBoG5LHnGJfVBnQzyYredV2kgl2b1pV/N0GxMOpBjV9OmTfVu8KIU5sBKJ7jq19wkp0iRHFh98itKh8V+fmn25d/+9rex2uYNViUHR9fn15bP1T5L5xcFKqt+peVr3769mjRpkhEziYMyzWa67bbbBtdU0q/JOGOEf+Sp5MBKjrfkuKFDmAOrND99L8mtD3sv1R4Vvb5oPUiVl/LzoQ8tZ1HbX0l+/DkMW+FAs+Bb/lJW6ReydF1We8rvKRGXtlckk2T9k35+YU9VsAoFzf4dFdI4MNCPSUjfFOxnxf4l60knneT1nTKsPFL2ij/fSX50OG/ePNWwYcNAJD4Tu6/6x21WpSUPwzilSeMsKg1AxeXra8A3i3x58otjUulY0d4/SF7OzrYBYeWxl2jy6cDq63nT5ZKyLzo/vS1K/0DLo7eu9t63PrScReUnJZ9kf1eyv6bLZ2+Lqg/YU1tTKpjcYc899wwOJLHndg5Z2l+dRxH1oWWjrS97z++RJS5pD/j98yhvlvoiVV67PZL+XsxZZo2n7V/RfTjTsO8Rvvi5fs/OyijuOh/8wu7n630m7F5p01zb37pUX4gtf35c3vdtPbm2b5Sfr/psO/jRiqVXXHGFKQJNDEMO/xSy9At0Rpxtpe8HRf/+Jy2fZuR762IPpOufdH5R7Oz6ncUBO4/+UJT8NZ1eBAdWH89bXvXPRX9S7S/JkEd5XeyLC6eoayX5+fpeYtunLO2v/T5TtP64L/kk9ZvH8xFVT5Om11oHVgLAO4iffvppsIxsUjB5nZemwt18881q9913D0RL2nGmX5J1795d7bXXXqpVq1aqQYMGZUWLGujzyc9+gVm0aJGaOHFimWx5J6TRR6UBbnIYPProo00RiDNdQ866YYFmcaLZPXQgp4ooB1Z9jot+dR55bPNwYM37+QjjVqQOC39+7eVbw2TnH54qOThKPL9cPgn7LJ1fGCMX/UrLx/P74IMP1BlnnBEmsknjziSV9Gsuyhjx4RDGyytRXzIWLfIyH/Zesj3i/IpWXzRUyfL60IeWU2+L1v5K8uMO70n6mzTYS/0bHV577TU1bNgwvRu6dbGnoRlmTPRhr6Trn/Tzy/ODPS2vOJX69/wKXn8onZ6de+65R7Vu3VrRsuw6JJnFRJ/rY+tqr3idWbNmjTr77LNjxeQfQejHev369TPn87yk6l9am2WEyRDh8lcagIrLnve7a3oG1jz5xTGpdKyI7x9pZ2CdMmWK2m233UxRXR1YK73/8voq9bwZ4f8TcbUvdn5F6R/YcknY+zz0UVR+mqerfJL9Xen+mi4j37qWl+clGYc9LadZyZ6WX1Gawp/vtP2DIuqjtHTf70nb+7B7pElLYw+y6NdnebPUlzTlrfQ+w+8v/b04jQ6jzk3bN03yPcIXP1/9qyg2SdJ98Au7r6/3mbB7pU1zbX/rUn0httwmuL7vc11JtG8+6zNfrW7z5s2qb9++gfg77rijmjZtmimKy/cczjZJ/4CfL/H9nucnYa94fhLyGcgeIy72QLr+SecXhc12EMviwKrz9tkf0vco2rYIDqzERPp5y6v+uehTsv3No7wu9sWFU9S1kvx8fi+RaH/58yHRvkUxzZruQz5J/ebxfGRlp6+r1Q6s/CGwZ4HTAGp6yytcpSVXZ8yYoVq0aBGInMShIKxsBx10kDr99NNLlq6j8+6++241e/bskkt88pN4gSkRVmiH68N1Rlx7lo7zzjtP0TI/UcGeqYkGSSo5sNp5pdGvfa3PfQkH1qI9H2G8itRh4R+SbOeBMNl5g1rJwVHi+ZW2L9L5hTFy0a+0fNw55JNPPlEnnnhimMgmjcteSb/moowRPsBrt7000zTNPqVDkhlY6Vxpfvr+Ulsf9l6yPSpyfdE6kCyvD31oOaO2Nd3+SvKbOnWqatOmjSlqmIONOfhd5LDDDlMXXnihSXryySeD2f1NQkiE26SXX35ZjRgxIuQs/0k+7JV0/ZN+fmFPVympGViphvK6vGHDBnXqqaeq4cOHqyOOOMJU4AkTJqjHH3/c7Nd0JK294n1EWpmEZpONC/z8tWvXBu+e+nwf9c9elv2UU05RGzdu1LcU3fKyJRmAiro576fXtANrnvyieCRJL+L7xx133KHoPVeHSu0lfXNp3ry5Pl2Fnc/bc9f3Xx/PmxE+IpLWvtjZcJtak/0DWy7a57Jlsfd56IPLWDR+NsMs8vHnoxq+1xVVH7Cn5U+46/d2l/5BEfVRTqg8xdXel+eYLoXbA9f2MsmdJcubpb7w8rraP94Plf5enIRlpXN8fI+Q5JdHe16JUdxxH/zC7sfrkeT7TNi90qa5tr91qb4QW26TXN/3ua4k2jef9fmiiy5Shx56qBF57NixatmyZWXLp9OPEOfPn2/OSxPhbJN8Pyj69z9p+dKwzHquiz2Qrn/S+UUxsR1YyVGJfmwvEVz6QzQrYbt27crEoJVqV6xYUZZeUwlFcWCVft7yqn8uepNsf/Mor4t9ceEUda0kP+nxLS6zRPtb9P64D/kk9ZvH88F1niVeqx1YbYfAsAGCLNAkr+Gz4tGy8v3794/Mnlf4rA6sOnNyNCCHAx3COtA++Um8wGjZJbeSBoDnpQc44rOdAQMAAEAASURBVGTldYHOy+LAqvNPol99bh7brA6snEnRno8wbrzDkmQGurA8pNJ4B4PyjBvMt19qKjk4Sjy/0vZFOr8wPbjoV1o+/guZb775Rh1zzDFhIgdp9pT1lfQbmVHCAz4cwqT5JSxK4tN82Huep+sARZHri4YsWV6eV11pf3mZXevLVVddpegX1jqMGzdOLV26VO+WbWk2xl69epn0JB/GXOypuZFAxIe94rqQqH/Szy/sqawDKzmn/vCHPzS1cfDgwep3v/udaty4cZDGZ/IwJxUkkvR9gQ+6fP3116p3796RJbBnQHrppZdKHNx91L+LL75Y/eQnPzEy0T3oHcJH4CzC3p+T3tPXgG8W+fLkl5RP2HlFfP+wV1ChZ4rqfFTgAxB0Ttj3Kcn3Xx/PW1TZ7PSk9sW+rij9A1su2ne193noo8j8iKGrfLyP5drf5XlJ9NeofHZwLa+dn9Q+7Gk5Sdfv7VnaXy1FEfWhZUuyzWrvk+Qdd45kexl3H/uYRHmz1Bdus1ztn8/vxTavLPs+vkdI8sujPc/CTV/jg5/Om299vc/we2SNu7a/dam+EGNuk1zf97nOJNo3n/W5Q4cO6qabbjIikwPdqFGjFLeRlcZczMUREc42yfeDon//k5YvAptosos9kK5/0vlFgerYsWPJard/+ctfSup61HVp0rP0h+wVafT9Ko376/Py2hbFgVX6ecur/rnoSbL9zaO8LvbFhVPUtZL8eF7S30sk2t+i98d9yMd14vo+mMfzEVVPk6bXagdWGnA/8MADDYskM0KZk3OK0C9ftt9+++ButMT8cccdF3pne9Y6VwfWli1bqunTp5t7hc1+4JOfxAuMEV4wImkAeOP1/vvvqzPPPDNS0uOPP16ddtppJcddHFiT6LfkZp53sjqwFvn5CEPGP958+OGHasCAAWGn5ZJmD6guXLiwZIlnLoR9biUHR4nnV9q+SOfH+ei4i36l5bNf+G677bZgQFDLyrejR49W3bp1M0mV9GtOzBjx4RAmzS9j0SIv82HvJdujItcXDVWyvD70oeWstK2p9leS3znnnKN+/vOfm6KuXLkymFHSJFgRWoqoadOmJpXalMWLF5v9sIiLPQ3LL2uaD3slXf+kn1/YU1kH1q5du6rLL7/cVEHq87du3drsF/H9UwuX1F7xQRe6lupk1Awkl1xyiTrkkEP0LdSiRYtKPpz7qH/2e1SSmaSMgCkjnEWSAaio7LkNlJyxKIt8efKL4pEkvYjvH8OGDVNHHnmkEf/pp59WV1xxhdnnEZqVmWZn5iHMgVXy/dfH88blj4sntS92HvzZqOn3aVs2V3ufhz6KzI94uson2d+V7q/Z9UWivGF5SqTBnpZSlPjenqX91VIUUR9atiTbrPY+Sd5x50i2l3H3sY9JlDdLfZG0f/Y3YMnvxTavLPs+vkdI8sujPc/CTV/jg5/Om295my75PsPvkTXOZcvSn6xL9YUYc5tE+y7v+3S9DhLtm+/6zJ1VyXn33HPPVbRCiQ5hY+b6WJItZ5vk+0HRv/9Jy5eEoes5LvZAuv5J5xfHhtc97Zwdd37aY1n6Q3b90feEA6smUbq1ebmO/+ZZ/0pLknxPsv3No7wu9iU5leRnSvLz/b3Etf0ten/ch3yS+s3j+Uhec8PPrNUOrNRJvvXWW1W9evWC0pPT51lnnaVoYLFS6NOnT/BxtdJ5rsftJfuoUofNcnXjjTeqPfbYw9wuyoGVZtqj5aRp2vW48LOf/UzRkvY60CAkNYg8+OQn8QLDZZWKSxqA2bNnqyZNmgSiVfr1Iv+1vy5LmAOrpH71ffLYZnVgLfLzEcaNf0B1/YVmWP5p0uxfsVAdpCVzaWktHjp16hQs9aztJB2r5OAo8fxK2xfp/DgjHXfRr7R8tg1ft25dmRO8lpt3pCmtkn71dVm3PhzCpPllLVvUdT7svWR7VOT6oplKlteHPore/krys9ts6nMOHDhQrVmzRqvLbGk2f/rVtQ5xP8bS59DWxZ7yfFzjPuyVdP2Tfn5hT2UdWKkOzpkzR22zzTah1ZFmKA57dkJPFkqUtlf8wzeJmKbPQT/g4+/evuqf/S4V9m5r46WPNbfccoudHLvPWSQZgIrKjPfNJAd8s8qXF78oHknSqe7QB3sdbOdonR63la5/9KzxAc64d376LrXrrruWiBfmwCr5/itdXhJe2r6UAPlupyj9A1suve9i733oQ8ult0Xn5yqfZH9Xur+mdcC3ruXleUnGYU9LaSb93l56Vele1vaXcimiPkgu3/ae7uESJNtLkiPP8mapL5L2z+f3Yhed6mt9fI+Q5JdHe65ZZNn64Bcmh6/3mbB7pU1zbX/rUn0httwm0b7L+z5dr4NE++a7PtMKOj169NAiqw8++EC1atXK7NMMZUuWLDH7aSOcbZLvB0X//ictX1qeWc53sQfS9U86vzge3EbHfaew8/DZH7IdMvW94cCqSZRupZ+3POtfaUmS70m2v3mU18W+JKeS/ExJfr6/l7i2v0Xvj/uQT1K/eTwfyWtu+Jm12oGVinzBBReoww8/3JSenMroozMZFjtQhaKBbJpZoUGDBsFshfQrVJ+BftV11FFHmVt88cUXasSIESVOZtdcc43q3LmzOYciUQ6s+sMbOanRFOM0648d9t9//2BmECqjDmPHjlXLli3Tu2bri5/EC4wRUjAiaQBuvvlmtfvuuxvpSCdDhgwx+xTZd999FTktN2rUqCSddsIcWKX1W3ZTTwm2MUw64Fj058PGdcMNNyhaOlUHeumlX7a/9tprOinXrf1Bl15Wpk6dqhYsWBDI0bNnT0WOFfXr1y+Rq5KDo9TzK21fpPMrgfLdjqt+peXjnUiS9fXXXy/5YQLN7k0vhnxmRDqvkn7pHJfgwyGM5JHm51JG+1of9l6yPSJ5i1pfNEvJ8vrQR9HbX0l+pJOrr75a7bffflo9asuWLYGjKm/PunfvHtgc/gMI6mPPmDHDXBcVcbWnUfmmTfdhr3zUP+nnF/Z0aGRV4XUz6n3Lvtj+6KKPZ5ntRV/rspW2V3zQRcsV9l5z3333qe22206fEvR/aYZKO/iof2SP6IdaPLz66qvq/PPP50lBnN71yfGenI5Jd6tXry47JyqBs0gyABWVDx9MKIIDa178ongkSS/q+4d+3nQZ6IfEJ598st4NtvYSdPpgmAOr9Puv9POmyyv1vUmz0Ftugymtpt+ntVx662rvpfWh5dLbovNzlU+yv+ujv6b1oLeu5dX5SG9hT98ySNN8bzcXhURc+gdF14cvex+CMVWSdHvpu33jhctSXyTtH8ni63sxL6dLXPp7hDQ/3+25Czu6VppfmDy+3mfC7pU2zbX9rWv1hdskzdrlfV/nIdW++azPO+ywg7rrrru0yCXbzZs3q759+5akpd3hbJN+Pyj69z9p+dIyTXu+qz2Qrn/S+UXxuO6669Q+++xjDm/cuFFNmjQpdPI0c9J3EZ/9oSI6sJJ/BvfhIBa/+tWvVPPmzQ0W8pt55plnzD5F6DsizWzrO0g/b3nVv6xcpNtf3+V1tS9ZOUVdJ8nP9/cSifa36P1xafkk9Ut1yPfzEVVPk6bXegdWAmHPKEJp5Mj12WefBVty5Nx2220Dp1U6pgMZH98OrHQvPhUz7dNg6aeffhpsaRCQOwbQcQpRA6q2USGHXcprw4YNiuJkFJo1a/Z9Jv/5H/erOjrFBz+pF5iSggjsSBoAe4k5Eo/qHQ3CNG7cOBg0jZqtic4Nc2D1oV+6l++Q1YGV5Cr688HZ0Wym5LAaFkj3OtDyI6NGjdK73ra0FNq1115b5qBa6YaVHBwln19p+yKdH2cloV9J+ewlX0lWbfPJKdl2XNVlqaRffV7WrQ+HMC2LJD+dp8TWh72XbI+ojEWtL5q/ZHl96KPo7a8kP9KJ3W5rPdHS3OScQ0sF2T++STr7KuUlYU+1TC5bH/bKR/3z8fzCnobXHP7xKep9y76S3q3ox5H2Oxs5c5NTd95B2l7xQRdeFupz0MofP/jBD4J3abv85LzKnd75tT7qX9THcLJbX375ZaAf0hX/4VaYAyv1cydOnFhyHslO5eN2j8pPg1p2eOWVV9Qll1xiJ5fsuwz4+pJPil9JQQV3ivr+YS89TUWmukEO7FQ/aAafhg0bhpIIc2ClEyXffyk/yedN2r6QfDwUpX/AZeJxCXsvqQ8uG8WLzs9VPsn+ro/+WrXoA/Y02/d2X+1vUfXh297bz0uWfcn2Urq80vVF0v4Ra1/fi7PoMewa6e8R0vxIZp/teRiTNGnS/MLu7fI+E5afZFqR+hu6XEWuLz7e96ncUu2b7/p85513qp122kmrymyffvrpYBIokxARkbb3Rf/+50O+CLQiya72QLr+SecXBYnuc/vtt5d9p+Tnh33zlO4P8ftFfXOqyRlY77//frX11ltzMRPFo34wn+jiFCdJP2951b8URSw5Vbq/5ru8rvalpPACO5L88vhe4tr+ErIi96+k5ZPUL8nm+/mge7iEOuHASr+WoFku27dvn4pVXg6sF110kTr00ENjZaOZ9ahDoWd3DOtcUAZ2ByM20+8O0qDK6aefrugXOFHBBz+pF5gombOmSxuA0aNHq27dulUUh/RATo00O64OSRxY9blR2yT6jbpWMt02hElnYCUZiv582Jwuu+wy9eMf/9hOLtkP+xVryQmCOx07dgycaqMGTulWJA8NxulfllVycJR8fqXti3R+tipc9SstX9QsQFxu0ie1GfRDDQqV9MuvzRL34RCm5ZDmp/OV2Erbe+n2iMpYxPqi2UuXV1ofPvpXuuwSW2l+JBP1TemXgnzG/ihZ6UdhxPyNN96IOqUs3dWelmWYIcGXvZKuf1Q06ecX9jS8wmRxYKWcaCYD/q5JDmzHHHNM+E08p0rbKz6gRT/Eox9ExtkF6nPQrE5/+tOfIkvqq/5deuml6uCDD468r30gzIE17COdfV3c/jvvvKMGDRoUd4pyGfD1KZ8Ev9iCOxws8vvHcccdp0477bTYwSGyCfTDYT44GuXAKvn+S8glnzdp+xJWJYrQPwiTS6e52ntJfWiZ+Lbo/Fzkk+7v+uivcV1Q3KW8dl5S+7CnpSSTfm/31f4WVR952PtSTaTfk2wvpcsrXV+k7R/R9vG9OL0Wo6+Q/B7hg5/v9jyaTLIjkvzC7ujyPhOWn3SaS/tb1+qLj/d90qdk++azPp944omK/uxA30Ppx6mVgrS9p/sV/fuftHyVGLsed7EHdG/p+iedXxQfcn7s379/7HcK+5uEdH+Iy1ZEB9Y//OEPwWRjXM4k8bwcWEkW6ectr/qXhKN9jo/213d5Xe2LzcBlX5qf7+8lru0vsSp6f1xSPmn9Ej/fzwfdI2vw4sBKS/IddthhRqaZM2eqe++91+zHRbIqgGY37N27d1zW6thjj1Wnnnpqycwp9gU0sECD7+RgR7/kzSvQMt69evUKvR3JQ0vj8AHVqPIeeeSRQTnbtm0bO7BI5fz73/8eOVtkmCCS/GjmrunTp5vbpHFoNBd5iPiof9RRpGUEowZ633zzzWCWHupMHnXUUaZUdueRDvjUr7mxh4iLAyuJUw3PB8dGy3CSPWrTpo3aaqutyl4SVq5cqYYPH84v8RonRwO6Hy2HoJ0Y9WzAS5cuVdOmTQtmCNMzAlda8tbH8ytpXwimdH5cQRL6lZSvZ8+e6swzzwy1MfTjBGo/aPmPFi1aBMXI04HVnpHRnqVq4MCBipbO1eGBBx4w5YibuU6Sn763xFbS3vtoj6iMRasvmruP8krqo+jtrw9+pBuy97S0ZuvWrbWqSrbkqPbCCy9knlVcwp6WCJRyhzuwStsryfqni+Xj+YU91XS/3yZ53yq94vs96uvTMlM60A/TRowYoXdz3UrbKz6g9fzzz6tZs2apMWPGhL5T00ynV1xxReKltHzUv4MOOihwvo+aiZ6UQauTUJ9j3rx5ZbrxMQBl34QP+JLjzHnnnWefErnvWz5XfpGCOx4o+vsH6YVm3g1756f3rquuuir43nPggQcaEmHv+/qg1Puvzo+2Es+btH3h8vF4TfcPuCx2XMreS+jDlk3vF5kfyZhVPh/9XR/9Na0Hvc1aXn299Bb29P+Ipvne7qv9Lao+8rL3/6eNbDGp9lK6vNL1xYf9I+LS34uzaTH6KqnvEb74keQ+2/NoMsmOSPELu5vL+0xYfj7Ssra/da2++Hrfl27ffNZne0bvNGMn0vZePwtF//7nQz5ddh/brPZAyyJd/6Tz03La2w4dOgTfm+i7vh775efY3ySk+0P8XvYPQfWxmpyBtRocWImT9POWV/3TOk669dX++i6vq31JyqfSeT74+f5e4tL+ch5F7o+TnBLy+dAvyeb7+aB7ZAleHFizCJL3NYcccojad999g19X0OD7mjVrglkIaVCupsL222+vqMNLcm3ZskV9+umn6oknnlCrV6/OJBL9knavvfYKnOhoyXpa9pBmzaEPdMuXL8+Up76oiPy0bEXdEjNy3qJfH5JuSRePPfZY7Oy3cWXxqd+4+2Y5tt9++6mrr77aXLpgwYJgZiaTkCBSTc9HguLU6Cnk4G7bFd74vfjii2rkyJE1JqO0fZHOTxqMlHz0Qkp5kcPGe++9l+uPMKSZpMlPil+ae1Y6l2SStPeV7pfleF2qL9L6qKb2N0vdiLqGnJq6dOkS9J03bdqknn322WD2+Kjzkf49Aen6R7n6en5hT7PXWppxlPpXOpCD5zPPPKN3a2wrYa/sAS36BTYF+oEafdymfgf9+GnJkiVq7dq1mcvqo/7R6hadO3dW7dq1Ux9//HHwvk9O93Y/OLPQtfzCusRPqv5R/4/you8wNBvv448/bmbuoVWBkjqwUtWSfv/l1VWivBL2hctULXEf9l5CH9XCr6hykg6K/v5WVHa2XFL1uVrsqV3+ou1L6KPo9l66vSx6eX3WsSJ/L66G7xESz5sv/VYDP19lL2q+Rakveb3vS+qhLtXnon//8yWfZH2Rzku6/knnJ1XeutwfkmIonY+P562o9U+anc6vrpVXl9t1S32WavleUpT+VRTzIstXpOejzjqwRlUcpIMACMgToF+gDB061GQ8depURQ6TCMUgYC/pQo7VNGMnAgiAAAiAAAiAAAiAQDyBHXfcMZjNXp/1+eefq379+undqt9GDWhVfcFQABDImUBaB9acxcPtEhCo7fY+AQKcAgKFIAB7Wgg1QIg6QADfi+uAklHEOk8A7/t1vgoAAAiAAAiAAAiAAAgUigAcWAulDggDArWTwIQJE9QPf/hDUzia3ZNm+UQoBgGaHZdmydVh4sSJatGiRXoXWxAAARAAARAAARAAgQgC1157bbCChj48f/58NWXKFL1b9VsMaFW9ClGAghCAw1VBFOEgRm239w5ocCkI5EoA9jRX3LhZHSaA78V1WPkoep0hgPf9OqNqFBQEQAAEQAAEQAAEqoIAHFirQk0QEgSql8BvfvMbddxxx5kCfP3116p3795mHxG/BObOnatWrVqlZs2aFSz3bN9t0KBBqmfPniaZllc9+eSTzT4iIAACIAACIAACIAAC5QRoiU3qM3Xr1s0c/Pbbb1WvXr3Mfm2IYECrNmgRZSgCAThcFUEL2WSoK/Y+Gx1cBQL5E4A9zZ857lj7COB7ce3TKUoEAlkI4H0/CzVcAwIgAAIgAAIgAAIg4IsAHFh9kUW+IFDHCDRr1qxk+VQqfsOGDcsoLFiwQE2ePLksHQl+CPCPEF999ZXasGGDWrdunWratKlq1aqVaty4ccmNx44dq5YtW1aShh0QAAEQAAEQAAEQAAGldt55Z3XLLbeoevXqqUaNGpUh+etf/6quv/76svRqTuB9yeeff16NHj26mosD2UGgxgjA4arG0Ge6cV2095lA4SIQqAECsKc1AB23rHUEeB8f34trnXpRIBBITIDbArzvJ8aGE0EABEAABEAABEAABDwRgAOrJ7DIFgTqGoHmzZuru+++O7bY69evV/379489BwdlCfCPEHE504xh5Fxcm5a8jSsvjoEACIAACIAACIBAWgK77rqruvXWW0Mvq62DPbwvWVvLGKpQJIKAMAE4XAkD9ZxdXbT3npEiexAQIwB7KoYSGdVhAryPH4cB34vj6OAYCFQ/AW4L8L5f/fpECUAABEAABEAABECg2gnAgbXaNQj5QaAgBOIcWDdv3qyWLFlS62akKgj6WDFotts2bdqoBg0aRJ5Hs7LSAMBrr70WeQ4OgAAIgAAIgAAIgEBdJxDm0PSvf/1LLV++XF133XW1Eg8f0HrmmWfUmDFjamU5USgQ8E0ADle+CcvmXxftvSxB5AYC/gjAnvpji5zrDgF8L647ukZJQSCOAN734+jgGAiAAAiAAAiAAAiAQN4E4MCaN3HcDwRqMYEf//jHJaX797//rWigG6HmCXTo0EF17NhR7bjjjqpFixZq3bp16sUXX1TPPfdczQsHCUAABEAABEAABECgSgjo/u4333wTOK5WidiZxezatauqX79+cP2yZcsy54MLQaCuE9hll13UfvvtZzA8+uijJo5IMQnUNXtfTC1AKhAoJwB7Ws4EKSCQlQC+F2clh+tAoHYQwPt+7dAjSgECIAACIAACIAACtYUAHFhriyZRDhAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARCoEgJwYK0SRUFMEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEKgtBODAWls0iXKAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAQJUQgANrlSgKYoIACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIBAbSEAB9baokmUAwRAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAASqhAAcWKtEURATBEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABGoLgVrvwHrjjTeqrbfeOtDXwoUL1Zw5c6pGdxMmTDCy3n///eqpp54y+0WJ9OzZU+27775ql112Udtss02JWBdeeKH6+OOPS9KwUz0EqqH+VQ9NSBpGIE/7jPocpoGaS4M+ao497pyNwD777KOGDRtmLr7tttvUM888Y/YRAQFOoEOHDurKK6/kSWXxKVOmqMWLF5elIwEEbAJ437KJ1Nw++i81x17qztL2WTo/qXIiHxAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARCoJgK13oH1oYceUvXr1w90smrVKjV06NCq0c8jjzxiZCXn1UoD4ebkHCKdOnVSo0ePVs2aNYu82wUXXKBeeeWVyOM4UGwCRa5/xSYH6ZISyNM+oz4n1Uo+50Ef+XDGXeQI/PSnP1UjR440Gc6fP1+RAyICCIQRoH7y+PHjww6ZtJkzZ6p7773X7CMCAjYBvG/ZRGp+H/2XmteBqwTS9lk6P9fy4XoQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQqEYCcGAtsNaKPEA2d+5c1ahRo1h6cGCNxSNycPLkyaphw4ZBXsuWLVNTp04VyZcyKXL9EytkwTPyqV+JorvKBwdWCS1UZx5FtC+u9dm3Jooun+/y13T+cGCtaQ1U1/3h0FRsfVWLPcX7VrZ65FO/Rey/ZKNUd6+Sts/S+dVdzaDkIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACdZkAHFgLrP2iDpCdffbZqlevXobcli1b1NKlS9V7772nvv76a5N+3333mTgifgg8/PDDql69ekHmL7/8shoxYoTYjYpa/8QKWAUZ+dSvRPFd5YMDq4QWqjOPItoX1/rsWxNFl893+Ws6fziw1rQGqu/+O+64Y4nQnTt3VsOHDzdpmIHVoMg9Ug32FO9b2auFT/0Wsf+SnVTdvVLaPkvnV3c1g5KDAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAjUVQJwYC2w5qdPn64aNGgQSLhw4UI1bdq0Qkg7adIk1b59eyPL6aefrtauXWv2EcmPgM8B2qLWv/zo1vydfOpXonSu8uXpwIr6LKFxuTyKqA/X+ixHJzynossXLnXtSYUDa+3RZU2V5Ec/+pEaO3asuT0cWA2K3CPVYE/xvpW9WvjUbxH7L9lJ4UpNQNo+S+en5cQWBEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABGorATiw1lbNeizXvffeq5o1axbcYdOmTeqkk07yeDdkHUfA5wBt3H1xLB8CRdevq3x5OrDmozHcpZoJuNZn32Uvuny+y1/T+cOBtaY1UP33h0NTcXRYDfYU71vZ60s16Dd76XClDwLS9lk6Px9lRp4gAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgUCQCcGAtkjaqRJY//vGPaquttgqkXbNmjaIlLhFqhgAGaGuGe153Lbp+XeWDA2teNQn3SULAtT4nuYfLOUWXz6Vs1XAtHFirQUvFlhEOTcXRTzXYU7xvZa8v1aDf7KXDlT4ISNtn6fx8lBl5ggAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgECRCDg7sHbv3l01atQoKNM//vEP9f7770eWb7/99lNt27YNjn/99dfqz3/+c+S5dGD//fdX3bp1U506dQpm/HznnXfU8uXL1d/+9je1fv36IK899tgjyOPTTz8NjtkZRjlI9e/fX3Xt2jXI95tvvlGU9z//+U81d+5cO4tc9rt06aJatmwZea8333xTvfXWW5HH9YFevXqphg0bBrtPP/20Wr16dRDPWl6en74H5VW/fv1gl2ZgnTdvnj5ktl9++aWaP3++2Y+K/PznPw/026FDB7XNNtuojz/+WL3++uvq+eefD/QcdV1Y+nHHHWeSFy5cqEg2CqTnPn36qJ122im4B9WVDz74QM2ZM0e99NJLwTlUL//rv/4riH/++efqqaeeUieffLI68MADFdXVZ555Jqgb69atU61btw7yo4Ep4vDqq6+qe+65R7377rvB9WH/dtxxR9WzZ09F5WzRokVQ7/7973+r9957T7322mtqxYoVofWX58XLp9N/85vf6Kj68MMPI5m//fbbsflL1T8jzH8irvrl9U+iPtvySe0XXb8S5fRR/3zZZ1/1+cQTT1Q//OEPFel7u+22U/QMf/LJJ4HdorbpwQcflECdOA+uE7JDZEfIlh1++OGK7NMOO+wQ2Biyp0888YRpD5LewPX51feR0oekPeDstJxS9lTn57LNQz4p/bqUk66thvaX5EzrwEr9Dt1Xousr9S/32Wef4Lnde++91W677aaob0r9ihdffFE9+eSTin4wVBNBon3zLTd/XtL2/6Jk86EPSYemrPLRu9hee+1lil2pXuoT+XNKaVHXSdWXumTviScvL+1TcHnfkn4/5/L56o/vvPPO6qCDDgr6We3btw/6MF988UXwfrNq1Sp15513fg/G+s+ff31Iqj2X6r9oufTWtf3NQx9aVqltVv3y+0vZF56njkvaZ8pTOj8tJ7YgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgUFsJODuwRjkghQG74YYb1J577hkc+vbbb4PBurDzKO2qq65SNGgUFujaMWPGqKFDhwbOgHTOv/71L3XCCSeUnW7LN378eDV58mQzg6h9ATkUnnXWWXay9/0HHnhANWjQIPI+5FB55ZVXRh7XB6TLy/PT90i6Pf/88wPnzrDzyRFk+PDhsWX+7LPP1OjRo9Ubb7wRlkVJ2i677KJuu+02k7Zo0SI1ceLEYLCTHFejAjldX3bZZYoGOvUAKDms0oBp06ZNSy4jx9chQ4aoqVOnGqdtfcLmzZvVOeecU+bATXnSHzm7VQp0TyovObSGhUceeSQsOVEaOT+T7FFBqv7p/KX0y+sfDV4X7fmtFv1qvbhsfdQ/X/qVrs/kRNK7d+/IdoNzJUeza665hid5i3OdkJPqCy+8oMjJNir84Q9/UHfddVfUYZMu9fzqDKX0IVlfODstZ9JtJXuaNJ+483zKJ63fuHIkOVYN7S+Vg7iNHDnSFIl+pDNlyhSzzyO33367atWqlUmiH/VQH2Ht2rUmjUeoX0oOglGB+r2zZ88OfiwTdY50umT7Ji0bz8+1/8fz0nFf+pByaHKR7/TTTw9+hKXLGleP9Tm0pT51x44dTdKll16qnn32WbMvXV/qkr0niLy8BmrCSNj7Fs+P+s/0zhwVkryf2/lJ98fPO+889bOf/SxKxCCd7OArr7yiRowYUXKez/ZSqv+iBZZqf33rQ8srtXXRL8kgbV/CyiVln3Xe0vnpfLEFARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAgdpKoJAOrL///e+DWS7joJOTIc1OpWcbTeLASrNX0SynjRs3jss6cEI888wzY8+RPig1QMYHtCTKy/NLW+YLLrggGGi0rxs2bJg68sgj7eTQfRqspPpAcsQF24GBnMho9tQmTZrEXRbM6jNgwIASB9bYC2IO0ixp3LmFTp0wYUIwk1DMZSWHqLzTpk0LndW2WgZoJfXL659EfS6BLbBTLfoVKKryUf986VfKnhI3sg99+/ZNjDAP50YtDNfJV199VeZYr8/j22XLlqmxY8fypJK45POrM5bSh2R94ey0nEm3eejYl3w+9JuUW9R53IE16pxK6b7bX7p/UgdWchKn2Y91oFndBw8erGgGdztQnuR8ttVWW9mHQvcrOaKFXpQxUbJ9yyhCostc+3/8Jr714erQJCFfs2bN1L333muKTSsf0IoDlQK3v9TekEMZD9L1hd/Ptf/ny57y8rvGeXnT5hX2vsXzq2Q30jqwuuqDl49+2HrFFVcEK1Pw9Lg42VO9wgid51O/Uv0XklOy/eX6ldQHySkZJPRL8kjbl7AyutpnO0/p/Oz8sQ8CIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACtY1A4RxYaVbOI444ooQzLY2ul3rv3LlziWOAPjGJA6s+l7aUJy1VT8u7durUSW277bb8cDDIFDUTZsmJQjs0eMdn/CS59Gy1dIssM7By0bKW96KLLipzAj3ggANUvXr1guxpEFvrht+P4jfddJP66KOPSpLtwRw6SDOT0Yw669evV+SIQOXms9GSo/IxxxxTko+9Yzsw0DV86d4tW7aoDRs2KHIkadGiRTAjKpWBuIQ5sJKD9J///OdgQPXQQw+1b6do+cz3339f0RKQ2vGk0oA+yfTBBx8EM7C98847iu7Rpk2bwNG2UaNGJfc499xzy2aepYFPkp0HctLVgcq2cuVKvVuypaXF58yZU5LGd6Tqn7R++QAtlzdrfeZ5SMT5gGqR9StRVh/1z5d+peozcbMdIzZt2qSWLl0aPMv0Y4h27doFy33Ts0whD+fG4Ebf/bNl0+k0k/jLL78cOLRS+9a8eXN9KNhSvX388cdL0mhH+vnVN5DSh2R98VGfdXkltj7k86Vf1/LaDqxFbH+pjEkcWO++++6S5+2TTz5RgwYNUmQ37EDLKdNMrbo/RcepHaF2nGaeJ/tCM17aM7PS0u30QxffQbJ98ymra/9Py5aHPuxncObMmSXOpFqWsK2kfLSSgG6z6F5nn322Iie4qEDLldM5OoTNNC5dX+qSvSeu0u9bnJ+0A6uuB7R17Y/ff//9auutt+ZZBu9rb7/9dtCfoiXjqZ9Fz7l+r7MdWH20l1ogqf6L/exT/i7vv1y/WlbauuqD5yURl9AvySFtX8LKZusojX3OI7+weyANBEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABGoTgcI5sNoznSxYsEDRUpk80DLr3bp140kqqQMrzXBJs2ORAwAPkyZNUu3btzdJtBwz3acmA3dOyurA6qO8f/zjH43T5rvvvqsGDhyYGJO9rC45dJ5xxhkl19MgPemDD2iGDZbzi2wHBn2MHGFuvPHGMmctmh3twgsvDByXaSDUdqAhJxRarpcCHzSjfXIuIUdrCn369FG0HKsOFOdLBI8bN07ttdde6tFHH411OKHzuDMqOZyOGjVKZxu5ffjhh43zCzms2ctqRl6Y4ECW+ietX3uA1kd9ToAi8pRq1m9koVIccK1/eeo3S32mWel+9atfGSJxzyXZLZq5e+PGjeqWW24x1/iM8DLp+4Q5t1111VWqS5cu+hTjuG8S/hORfn7t/Pk+l70o7Ztrfebl8xF3lS9P/aYpf7W0v3EOrDSr5ZQpU4Ifx+iyky045ZRT9G7Zls96SAfJ8Yjafd6HoHSasZ6WX9bOW+TkSvmGOcXS+VLBd/smJadr/0/LkYc+XBykJOWj2VPpudNh4cKFivKPCvQexh2pw35kJV1ffPcPXO1pFCvJdJf3Lc7PhwOrRH+c3r969OhhkEXlSSeQjR0zZkzwTmU7sJoMWMSnfrP0X6TbX65fKnYUu5r8viGpX2n7wqqKibrYZ5MJi0jnx7JGFARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARqJYFCObDaAx1xA24zZswomY0yqQNrlKOMPQBOjgQ0M2dNhiwDZPaAlo/yZh1QJQdNGoDSgWZFJQfQsNC6dWv1+9//3hwih424WVht/dGFlD85lIUt22sy/k/EdqCh62iGVQqnnnqq6tev33/OVIrPyEJOa3fccYc5dtlll6l//OMfZj9NZN68eaphw4bBJXFseJ5FGqD1od886jPn6TNeNP1KlNW1/uWp3yz2dOzYscGspJoVOYiTo3hRAi8TyRS2jLqW9b777itxrqMfaNAPNXTw8fzqvMO2XPaodsq+znd9ca3PtrzS+y7y5a3fNGWvlvY3yoG1ZcuWwQ+tmjRpYopdqQ/ZtWtXdfnll5vzaSa+448/3uzbEduZfsmSJYoc04sSsrRvUrK79v9Ijrz0kdWhyYd83J589tlnJT/WsHXDz6VZhU888UT7lFT7SepLXbf3BDTr+xZdy/nFvU/Tudw5mhwhacZdO/D86FhUu20/j1G2kH5IeOeddxrHfMqTbOLy5cspGhnoB6xk/yoFXmdr+gd+PtpfaX1U4pn2uG/9xsmTxL6EXZ/VPoflRWnS+UXdB+kgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgUFsIFMqBlQaydtppJ8M2biDr17/+tTrppJPMuUkdWPv37x8sVW8uZBFyTGzatGmQQsuxc6dFdlpuUQkHHx/lzTqgesEFF6jDDz/c8Ks0q6q9xCnNpPrYY4+Z63nEHjClYzSARXUqSbAdaH7xi1+Yy2j2M1qeUgdyGuGDp1xPNFtr2NLc+tq4rT1LDpch6roiDdD60K89QOujPkexlU4vmn4lyuda//LUL39OoxwvbCbUBpHjkA5pbIq+xueWl4nuc/HFF6vnnnsu9Ja0/DN3SrHtr4/nN1SQ/yRy2ZPqw3d9ca3PceWVOOYiX976TVPeaml/wxxYySbQjMuNGzc2RX7vvffUWWedZfbDIvbM7uPHj1eLFy8OO9Wk/eEPfzD3iXIKMyfnHMnSvkmJ6Nr/Izny0kdWhyYf8nGnRWIwdOhQRY6OdjjhhBNKZhIOWxnDvqbSfpL6UtftPTHM+r5F13J+PhxYXfvj9CPRvn37kqhBWLNmjaJ+ilRwaS8ryZC2/+Kj/eX6JXld9VGpzGmP+9ZvnDxJ7EvY9Vntc1helCadX9R9kA4CIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACtYVAoRxYaenjRo0aBWw3b95cMrAVBpwPICVxYK00wxVf3o+Wnu/du3fYbXNL4+XL4uDjq7xZB1Svv/56tffeext+UYPl+gRyIKbZT3Wg+/LZTnU6bW0HhrT64w409myvNNsPzVaogz1zIR8kpVljH3zwQX1q6LZDhw6qXbt2qlWrVqpevXrmHHLubdOmjdmvNgdWH/rlA7S+6rMBLhSpFv1KFJfX/SwzXOWp3yz2lBwCfvnLXxpUX331lfrf//3fzLMsm4yEIrxMJBstCx0Vmjdvru6++25z+NVXX1Xnn3++2ffx/JrMQyJc9qK0b671OaSYokku8uWt3zQFr5b213ZgXbp0qTrggANMv5XK/NZbb6khQ4ZULD49i/RM6nDXXXcFS0Drfd43qF+/fpBMs9Bvv/32QTxtH0fn67qVbN9cZdHXu/b/KJ+89JHVocmHfN27dw+cVjVH+0cNOp36tbQqgg6nnXZaopUN6HyX+uK7f+BiTzUL39us71skF+cn7cAq0R+nFTloZlIdaJ9sqlTwqd+0/Rcf7S/Xr4Q+pLjrfHzrl+7jYl+0nHyb1T7zPHhcOj+eN+IgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgUBsJFMqBlQ82ffzxx4qWTI0LDzzwgGrQoEFwShIH1kozVvHZiKKWUIyTR/pY2gEyuj8f0PJV3qwDqvYMu5UcNA8++GB16aWXGqzLli1TtKR3WLAdGNatW6dokD1p4A40tmNIly5dSpbqtR1vOfNZs2ape+65p+y2NGMwOUTzJYbLTrISTjnlFLVx40YrtXSXPzNZHAhLcyvdS1v/fOiXs/VVn0tLnW2vGvWbraSlV7nWvzz1m7Y+U0nbtm0bzK7Incko/YsvvlBvvvmmevbZZ9XTTz8dOmsdnec78DJt2LChxOE/7N5cX7aN9PH8hsmg07jsWRxYfdgDzkfanupyu2xd5Mtbv2nKWS3tr+3AGlZG0tFtt90WdqgkjfdfSw6k2ImbcS9FNhVP9dW+VbxxwhNc+390m7z0kdWhyZd8PN8oJzhuqyvZXWIpVV989w9c7CmVM4+Q9X2LZOP8pB1YK9WDJO/TtmN0pXfCtLx96pc/E0n6Lz7aX65fCX2k5VvpfF/6lbIvYfJntc9heVGadH5R90E6CIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACNQWAoVxYG3ZsqWaPn264frOO++oQYMGmf2wCF9ONYkD6xtvvKHOPffcsKyCtCQDbpEXeziQdoCMROADWr7Km3VAlevLnuU0DB85j02ePNkcipvZzHZgWLlypRo+fLi5tlKEO9Bs2bJF9enTx1xCs8bS7Dk6DB48WK1evVrvljg+UBlpJjUdaHaY6667rmSGNn2s0pZk+uijj2JPK9IArQ/95lGfYwFXOFjN+q1QtESHXetfnvrNYk8JAj3vPXr0iOVBNuOll15S1IZUemZjM0p5kJeJbBLJGhe47badlXw8v3GycNmTOIBQXr7ri2t9jiuvxDEX+fLWb5ryVkv7m8SBlco9cuRI9eKLL8Yi4PU/9sSYg3ZfJObUTId8t2+ZhAq5yLX/R1nmpY+sDk2+5LvqqqsU/UhLh1GjRqkVK1boXcWfTUqcOXOmuvfee81xHpGuL3Xd3hNb3ma/++67auDAgRx5bJzzk3ZglXi/TNsmxRY25KBLexmSXUkSfx6T9F/SljXJ+y/Xr4Q+SgoosJO2zJVuKW1fwu6X1T6H5UVp0vlF3QfpIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIFBbCBTGgdUerKHZ7X7729/GcuaDI0kcWCUG8GIFEj6YdoCMbs8HtHyVN+uAKp/pKekMt5xB3AwztgPD4sWL1fjx4xNrhA/SV3JgpQFkGkjWgZeL6qR2YG3WrJmiWXcaNWqkTw225Di2fv16RbMMf/755+YYOco2bdrU7FebAyvnIKXfPOqzAZ4yUu36TVnc0NNdHQTy1C+3JUkcDniBe/bsGczovPXWW/PksjjN3nzNNdcoyj+PwMuUxGl/3rx5qmHDhoFo9kzTPp7fOAZc9qT68F1fXOtzXHkljrnIl7d+05S3WtrfKAdWmpGZ2wbbOdxmYfd3qc/xwgsv2KdV3Kcf1mzatKnieVlOyKN9yyJX2DWu/b889ZHFocmnfLY8NKP4FVdcYTDTDwvpB4YU4vp1PupLXbf3xDzr+xZdK81POj/eJn311VfquOOOI7HFgkt7WUmItP0XXta454jfl98j7P1XWh/83hJxXmZX/fqwL2FltO1hnMN+2PV2mnR+dv7YBwEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAIHaRiBXB9abb75Z7b777gHDsAEcPlhjL28cBp4P7MGB9XtCeQxoce5pZgSaNWuW2nbbbSP1b+t4zz33DGY01OmvvfaaGjZsmN4t2doODIsWLVITJ04sOSdux4cDDTlgH3300ea2NIBHMzSSc21YIIfbTp06mUPV5sDqQ7951GcDPGWk2vWbsrihp7s6COSpX96+JHWYtAtNM9V1795d7bXXXqpVq1aqQYMG9inKdaC+LMOYBF6mNWvWqLPPPjvm7NJlssl5vl+/fuZ8H8+vyTwkwmVPqg/f9cW1PocUUzTJRb689Zum4NXS/oY5sJJObrvtNjV79mzVpEkTU+xKDuW8/n/66afBsuvm4gJE8mjfpIrp2v8jOfLSR1aHJp/yzZ071/zQavPmzapv376BanbccUc1bdo0o6a4Wb591Je6bu8JfNb3Lbo2Db9K7+dp80uyoknaNolkSBNc2stK9+HPY5L+S9qyJnn/TaPfJPqoVOa0x9OWOS5/H/Yl7H5Z7XNYXpQmnV/UfZAOAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAArWFgKgDa9wS7wRsxowZqkWLFgG7MAdWPlBnO9eEAecDSHBg/Z5QHgNaXE9pHFinTp2q2rRpY1T5i1/8wsTDIocddpi68MILzaEnn3wymN3QJLCIqwODDwcaWmaVZo2hQPX9vPPOU7TMY1Sg2Vp32mknc7jaHFh96DeP+myAp4xUu35TFjf0dFcHgTz1y9uLJA4HoQW2Eg866CB1+umnK5ohj4e77747cGjjaT7ivEw0E+NJJ50Uext+/tq1awPZ9QU+nl+dd9iWy5JUH77ri2t9DiunZJqLfHnrN025q6X9tR1Yeb2lGdSvu+46Va9ePVN0cv4j58CwwJ0G7Vnfw87POy2P9k2qTK79P5IjL33YDk3k5HXPPfdUROFTvosuukgdeuihRoaxY8eqZcuWqcGDB6sePXqY9ClTpqj58+ebfR7xUV/qur0nvlnft+hazs/1/dzOT2KFj8mTJ5f0nU455RS1ceNGupVIcGkvKwmQtv/io/3l+pXQR6Uypz0uqV8f9iWsPFntc1helCaVH71jtGvXruw2tJLLihUrytKRAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAALVSsDZgZUvSUzLovfv3z+SBR+ADXNg5QMUlEncYJY9KAAH1u+x5zGglXVA9aqrrlI0g6EO48aNU0uXLtW7ZVuaTbBXr14mPW6g39WBwYcDDdfFhg0b1KmnnmrKEhbhzxIdT+vAGjdDbdj9KqWlHaD1oV/OsGgDtFy2atRvJf0nOc4dBLLUP87Qt37T1uck5dfnkKM9OdzrwB3bdJqPLS/T119/rXr37h15G3tGr5deeqnkBwI+nt9IYb47wGVPyst3fXGtz3HllTjmIl/e+k1T3mppf20HVnLmI6c+HciZvU+fPnpXffPNN2rIkCGKZq60g/2DlUo/6LGv973PnzVf7ZtUGVz7fyRHXvro2LFjyeoAf/nLX9RNN91UEYVP+Tp06FAiAzlEjRo1SvF3MqrLxxxzTKScPuoLz9NH/8DFnkaCED6Q9X2LxODvFK7v55SftD4uvvhi9ZOf/ISyDgLVcZJZKvjUb9r+i4/2V1ofUtx1PpL65WX12R5ltc+6zPZWKj/qZ+y222529qrSc112ARJAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAoOAEnB1Yaeag7bffPihm3NLJNDvV9ddfb3CEObDaS6gvXLiwZAl5c/F3EftcOLB+T4cP8vgY8KW7ZB1QPeecc9TPf/7z7wX97n+lJXZnzpypmjZtas4nnS9evNjs84irA4MPBxo+ePr++++rM888k4tcEj/++OPVaaedVpKWxIGV6+LDDz9UAwYMKMnDZSftAK0P/eZRn7Myqnb9Zi03v861/uWp37T1mZezUrxly5Zq+vTp5rSXX35ZjRgxwuz7ivAy0T3iZsi75JJL1CGHHGJEWbRoUYkjlY/n19wsJMJlL4oDq2t9DimmaJKLfHnrN03Bq6X9reTASmW2Z9qLmhmZfsBz4IEHGkxxM8ybk3KM5NG+SRXHtf9HcuSpD277tLNoJRa+5ePOqvRjiHPPPVfRDIo6VGrTfNQX3/0DF3uqufjechnTrHhBckm+n1N+0vqw33uSrLxCciQNnB3ej25Q9CMmCmHfX5IyTXOepH592JeosmSxz1F5UbpEfnBgjSOMYyAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAArWJgLMDq71EXNSsmjfeeKPaY489DLuwARR7FiAaRB06dKiipQ956NSpU7CUPF+qFQ6s3xOSHmDk3HWcDwqmGVDdeeed1R133KGzCQbRBg4cqNasWWPSdIRmM6RZDXWIc46mc1wdGHw40MyePVs1adIkKEKl2RH57MS6zEkcWPkAdaUZsnS+Sbd80C2Jg5kP/eZRn5PysM+rdv3a5cmy71r/8tRv2vpMPGjZzk8++UTRMp1x4Wc/+5k677zzzCn2zIzmgHCEl4myXrduXZkjvL4lt9uURg715Fivg4/nV+cdtuWyJ7EvlIfv+uJan8PKKZnmIl/e+k1T7mppf5M4sDZv3lxNmzZNNWjQwCCg5dhpWXYeqM9y6623Kt2PpT7xWWedVfJM8vN5nGZ5pefZZ8ijfZOS37X/R3LkqQ9uiyv1DTUj3/INHjxY9ejRQ99OffDBB6pVq1Zmn2aQXLJkidm3Iz7qS12398SY15U071t0reT7OeXnQx/2u0+SvhP9GOOWW24hkWKDS3sZm/F3B9P2X3y0vz70UancaY9L6deHfYkqC3/mktrnqLwoXSI/OLDGEcYxEAABEAABEAABEAABEAABEAABEAABEAABEAABEACB2kTA2YGVZuk56qijDJMvvvgimHmOO51ec801qnPnzuYcioQ5sFK6PeBGgwc0o9WCBQvosOrZs6eipeXr168f7Ot/cGD9nkQeA1p8MCbtgOrVV1+t9ttvP602tWXLlsBRlZYf16F79+6BM5h27KD0OXPmqBkzZuhTyrauDgw+HGhuvvlmtfvuuxtZ6Zmg5YR52HfffYOZvxo1asSTg3gSB9Ybbvi/WXXoInI6oJlqOc+yjBMmpB2gpWyl9ZtHfU6Io+y0atdvWYEyJLjWvzz1m6U+6x9e0LM7a9YsRbMk2mH//fdXV1xxRYnDGjmrkdOa78DLpO8VZmfuu+8+td122+lTAvswbNgws68j0s+vzjdsy2UvigOra30OK6dkmqt8eeo3Tbmrpf1N4sBK5T7yyCOV/XxNmDBBPf744yVYLrjgAnX44YebNPoRCvV1yPHKDtTHIU5du3YNbA3VBVqlwFfIo32Tkt21/6flyEsf1113ndpnn330bdXGjRvVpEmT1NKlS01aWMSnfDvssIO66667wm6rNm/erPr27Rt6TCf6qC+++weu9lSX3ecB/fXDAABAAElEQVTW5X1L+v3chz7ofY9+qMrDq6++qs4//3yeFMTJ9tEPG7fZZhtFDterV68uO4cn+NRvlv6LdPvrQx+cn0RcSr8+7EtU+bLaZ5/5wYE1ii7SQQAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEahsBZwdWAsKXdqN9ck799NNPgy05zXBHRDpOIcqBde+991bXXnttmYPq91dF/69WB1YaeJ84cWJZeYkZd2okxwYaRLbDK6+8omhpaB3yGNByGVC1Z6HRctPSkTTLIS3FzctNxyvNvkrnuDow+HCgocHWyy+/nMQzgRyyycm0cePGwSAsDcRGhSQOrDQbMTmshgW6lw60/OuoUaP0rtlK1z9p/eZRnw2MlJFq0G/KIqU+3bX+SetXuj7bg+Zkh6lt27Bhg6I4Of00a9ashFvcLKglJwrscCcKnh3JRjPH/uAHP1DbbrttWRtMznVhTu7Sz6+0PqTrC2dGcdf6bOcnve8qn7R+pcpXLe1vUgdW4kJO7QcddJBBRO3xGWecEcySbBK/i9gz1NExOvezzz4LtjSTKz3DfEZXOse3A2se7RuVQyK49v+4DHnog57D22+/vcwuczmi3pF8ynfnnXeqnXbaiYsRxJ9++umgPpcdYAk+6ktdt/eE1+V9i66XfD/3pY8o5zx6L/zyyy+D54T6WfyHq0kcWF3bS+n+i3T760sfVG8kg4R+fdiXqDK62OewPCXyi2K4fv161b9//7DbIg0EQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEqpKAiAPrRRddpA499NBYAK+//nrgtLrnnnsG50UNztLBjh07Bk55DRs2jMyTZpmjAS1arpVCtTqwhg3KRBY65MA777yjBg0aZI7kMaDlOqBKdYVmkrIdMkwhWIScOEaPHq3eeOMNlloedXVg8OFAQ1KS7N26dSsX2Eoh52RyMqXZHHVI4sBK51522WXqxz/+sb4sdBs2KyOdKF3/KE9J/eZRn0nmrKHo+s1arjTXudQ/af1K12fbgbUSF3qOTz/99GBGvUrnShznDqzkGE8OtXF2ldpdmuX8T3/6U+TtJZ9faX1I15cwCC71OSw/6TRX+ST1K1W2aml/0ziwEhuatZmcT3X48MMP1YABA/RusKU+7Lhx41T79u1L0ivt+HZgpfvn0b5VKmeS4679P36PvPRx/PHHB45HYT/w0/L84he/0FGz9SnfiSeeqOjPDtRfpx/LVQrS9QX23t2BVfL93Kc+Lr30UnXwwQdXqmLmeBIHVjrZpb2U7r+QPJLtr099kKySQUK/0vYlrnxZ7XNUnq75wYE1iizSQQAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEahsBEQdWgnL22WerXr16hfIh50NaypAv50czTPXu3Tv0fEokR5zhw4cHS7BrBwC6hpx0aJnNadOmBUut6hkswxwDKJ+sAzyV5KO8JYL0AFke5eUOrOSYfN5556VGQTOtXnPNNap169ah15Kj1QsvvBA6a2jYBZTf9OnTzaFFixYFM9uahAoR7kBjz/hKswJff/31JoeBAweqd9991+w/8MADxmlsxowZQb00B7+L0MDVySefbM7hxyj+5ptvBrPo0iwqRx11lDkc5rxgDloRWqaRnqc2bdqorbbaqmxmr5UrVwbPk3WZFwdWuoeUfvOozzaTtPtF1m/asmQ9P2v9k9avtD2lpcCPPfZY1bZt28jnl5jRjKd///vfI2dDzsq10nXcgfX5558PHObGjBlTNos15UMzmdGskCtWrKiUrdjzK60P6foSBSJrfY7KTzrdVT4p+yxVrmppfw855JCSGe/nz5+vyLEkKtCPsX73u9+VtMdhfQS6nuzMqaeeGvrs6vzJzlB/mvo3NLNiHiGP9s21HK79v7D756GPDh06BP1n6gfrdxkuS1wf0Jd89oydUT8O5HLyuGR9gb0vdWDN+r4l9X7uWx80YzU5Szdt2pRXqZI4zX5P71zz5s0rSY/bydpeSvdftIxS7a9vfWh5pbYS+pW0L5XK5WKfw/J2yW/SpEmhP3LBDKxhpJEGAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiBQzQTEHFgJwvbbbx84xO27775qy5YtwVLLTzzxhFq9erUzI3IgsvPhgzcvvviiGjlypPN9kEH+BGhQq0uXLqpx48Zq06ZN6tlnnw1mI81fEr93JOcXcoal2cJoGXJyxn7sscdym63Rb+mic4d+N0bDwZGqIUDOaHvttVfgJE62imbOo2eYHMqWL19eI+WwHVhphioKtGQpOd+SIwj9uGPJkiVq7dq1mWSsK89vJji14KK6ot9qan9JVupHk52hH/OsWbNG0Szq5KReU6Ga+EkzKqI+eBmLKF9dri9cN0WJ+3w/91FGWo2ic+fOql27durjjz8O7B/9qNH+DuDj3nnmWVfaX5upq35hX2yi2AcBEAABEAABEAABEAABEAABEAABEAABEAABEAABEACB2kFA1IE1TyT2kqHkCHjjjTfmKQLuBQIgAAIgAAI1RiDKgbXGBMKNQQAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQCAFgap1YL366qvVfvvtZ4o6ceLEYFlVk4AICIAACIAACNRiAnBgrcXKRdFAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAoA4QKKQD69y5c9WqVavUrFmzguXkbT0MGjRI9ezZ0yTT8oInn3yy2UcEBEAABEAABGo7ATiw1nYNo3wgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgULsJFNKBlTvlfPXVV2rDhg1q3bp1qmnTpqpVq1aqcePGJVoZO3asWrZsWUkadkAABEAABECgNhPgbeXzzz+vRo8eXZuLi7KBAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAjUMgKFd2CN4/3tt9+qBQsWqClTpsSdhmMgAAIgAAIgUOsIwIG11qkUBQIBEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEACBOkWgkA6skydPVm3atFENGjSIVAbNyjpu3Dj12muvRZ6DAyAAAiAAAiBQWwlwB9ZnnnlGjRkzprYWFeUCARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARCohQQK6cCqOXfo0EF17NhR7bjjjqpFixZq3bp16sUXX1TPPfecPgVbEAABEAABEKiTBLp27arq168flH3ZsmV1kgEKDQIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgUL0ECu3AWr1YITkIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgEAUATiwRpFBOgiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAgBcCcGD1ghWZggAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIRBGAA2sUGaSDAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAh4IQAHVi9YkSkIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgEAUATiwRpFBOgiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAgBcCcGBNifXGG29UW2+9dXDVwoUL1Zw5c1LmkPz0CRMmmJPvv/9+9dRTT5n9vCL77LOPGjZsmLndbbfdpp555hmzLxkpQnkly4O8QAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEwglUtQNrt27dVL9+/UzJHnzwQfX444+bfR+Rhx56SNWvXz/IetWqVWro0KE+bhPk+cgjj5i8yXn1yiuvNPt5RX7605+qkSNHmtvNnz9fTZkyxexLRopQXsnyIC8QAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAIFwAlXtwDpjxgzVokULU7LBgwer1atXm/2wyOTJk1XDhg2DQ8uWLVNTp04NOy0yDQ6scGCNrBw4AAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgkIhA1Tqwdu3aVV1++eWmkG+99ZYaMmSI2Y+KPPzww6pevXrB4ZdfflmNGDEi6tTQdDiwwoE1tGIgEQRAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAIDGBqnVgvfXWW9Wuu+5qCjpq1Ci1YsUKsx8VqSYH1unTp6sGDRoERVm4cKGaNm1aVLG8pf/0pz9VI0eONPnPn+/PgbUI5TUFRQQEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQMAbgap0YO3YsaOaOHGigfLhhx+qAQMGmP24SDU5sMaVI69jeTqw5lUm3AcEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQKBmCVSlAys5r5ITqw7jx49Xixcv1ruxWziwxuIpOwgH1jIkSAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEHAkUGMOrDvvvLM65ZRT1DvvvKNmz56duBitW7dWv//97835mzZtUieddJLZ55HjjjuO7wbx3/zmNyaNZm6dP3++2eeRt99+Wy1fvpwnBfGHHnpI1a9fP4ivWrVKDR06NIj3799fde3aVTVr1kx98803Qbn++c9/qrlz55blEZbQpUsX1bJly7BDQdqbb76p3nrrrcjj+kCvXr1Uw4YNg92nn35arV69OohnlS+tA2ufPn0MH7pxVPmLWt4A1n/+7b///qpbt26qU6dOgV6prlKd+Nvf/qbWr1+v2rZtq/bYY4/g7E8//bSkvgwaNEht3rxZ3XHHHTxLxEEABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABL4jkLsD6//8z/+ovn37qjZt2gQKICfPSy65JLEyxo0bpw488EBz/tSpUxU5lYaFRx55JCw5URo5iw4ZMqTsXNuBlWZ/nTx5stpqq63KzqWE9957T5111lmhx3jiAw88oBo0aMCTSuJPPfWUuvLKK0vSwnak5UvjwHr77berVq1aGbG+/PJLdc4556i1a9eaNB0panm1fFdddZUiJ9uw8O2336oxY8YEzsstWrQITvnXv/6lTjjhBHP6jBkzFB2jc1999VU1c+bMEgdXcyIiIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIFAHCeTiwLrLLrsEs60efPDBZY6eaRxYaXbTe+65R9WrVy9QFTlIHn/88ZFq8+3AumbNmmDW1MaNG0fKQAfef/99deaZZ8ae48OhU0K+pA6sd911l9phhx1MGT///HM1ePBgtW7dOpPGI0UtL8lIM/zSTL9x4euvvw5m2tWz3UY5sPI86Jy//vWvasqUKTwZcRAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARCocwS8OrAec8wx6thjj1U777xzJNgnn3xSXXPNNZHH+YELL7xQHXbYYSZpzpw5ima6jArDhg0LZsHkx/nsreRkuXLlSn7YxFesWKEofzvwGU75sQ8//FC9/vrrqn79+sGS89tuuy0/rEiW1157rSSN71xxxRWqadOmJony2XPPPc1+lhlYzcXfRbLKl8SB9e6771bNmzc3t/vkk0/UoEGD1KZNm0yaHSlqeYcPH66OOOKIEnGJ3UsvvRSkde7cucRRV59oO7DSzMB6lmF9jt7SrKxvvPGGmj17tlqyZIlOxhYEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAE6gwBcQfWtm3bqtNOO0396Ec/Ug0aNAgF+c033wQOgeTA9+yzz4aeE5bIZ+3csmWL6tOnT9hpsWkPP/ywmcH15ZdfViNGjIg93z5oO7CSMyLNPjp37tySUydNmqTat29v0p5//nk1evRos58kwmeQzerA6ipfnAMrzYhLs4lut912pjgbN24MZts1CSkiRSgvr2Mk+oIFC9TkyZNLSkF67NatW0ma7cBKB7t376569+6t2rVrZ+pcyUXf7dAswk888YQiJ2BihwACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACdYGAmAPrcccdp3r16hU6O6UGuXbt2sAh0Hb21MfjtjSjZ8+ePc0pf/nLX9RNN91k9pNGpB1YoxxLd9llF3XbbbcZsWgWzwEDBpj9JBEJh05X+aIcWFu2bBk4djZp0sQUJUsZzcXfRWq6vIMHD1Y9evQwIq1atUoNHTrU7PMIzfzbokULkxTmwGoOfhc5/fTT1X//938rcvqNCqtXrw5mZf3b3/4WdQrSQQAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQKBWEHByYKUl7k899VTVpUuXyNlWv/jii2CZ9Pvuu0+RA2vWMG/ePNWwYcPgcprB9ZhjjsmUlbQDa//+/dX69etDZZk5c6Zq2rRpcOzzzz9X/fr1Cz0vKlHCodNVvjAHVtLFLbfcoho3bmxEf++999RZZ51l9rNEarq8d955p9ppp52M6Jdffrlavny52eeRX//61+qkk04ySZUcWPWJHTt2VCeeeKI68MADI5+Zr776Sv39738PZmX96KOP9KXYggAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgECtIZDZgfXaa69V++67bygIcjB95ZVX1Jw5cyIdAEMvjEi0nQWjZhWNuLwkWdKBlZZ/P/7440vy5zu33367atWqVZD09ddfB8vJ8+OV4q4OnRLy2Q6sS5cuVQcccIBq1KiREf+tt95SQ4YMMftZIzVdXpoZWJdr8+bNqm/fvrFF4fImdWDlGR599NHq2GOPVW3btuXJJXGqr3wm35KD2AEBEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEACBKiWQ2YHVXkKdyk9LyD/66KOB46okD3KE3WabbYIsv/32W3XaaadFznpa6b6SDqxU3gEDBkTe8oYbblA0Sy0FkrtXr16R54Yd4A6SSZ12H3roIVW/fv0gOwn5bAfWMDmlnCxrury8bnz88cfq5JNPDiuuSXvggQfMLKpZHFhNRt9Fzj77bPX//t//U9tttx1PVv/85z/VJZdcUpKGHRAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARCodgKiDqwbN25UixYtUv+fvTMB22M6G/+RhkTQEELEHoLYukiV/HHxof0qqK32rWpprImIiK2E2kkpiaXEltqKLqJVyUfLp8S+RCyxVOyN2Hfl757PmZ7nvDPzzHKf553nze9c1/vOzJmZ+zn379xnmTP3nHPZZZepcfnv//7vhtk9H3/8cXPkkUeWlu86Kc6YMcOMGjWqkCzXQfS5554zhxxySOr9ne3AqpG+PA6sAmD06NFm+vTpqSzynKjqwFpF30UXXdRcfvnlcTJnzZplhg0bFh8n7fzud78zPXv2jE5VcWBdYokljMwy/P/+3/8z888/f8NP4cDagIMDCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEOgiBEo7sJ5wwglm7bXXTsQgs43KsvI33nijuf322xOvyRt55ZVXmkUWWSS+/IADDjAvvvhifFx0R9OBdebMmWb48OGpSehsB1aN9KU5sH700UcNzpYff/yx2X777VNZ5DlR1YG1ir7LLrusGT9+fJzM559/3hx88MHxcdJOVQdW4bX55pubxRdfPEl8NGuv/IbrWJt4IZEQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIE2I1DagVX0lJkjd999d7PeeuuZHj16JKr+2WefmQcffNBcffXVRhwMi4R11123Yfl0cYo96KCDiojocC0OrP9BksfBNsmBVRheeOGF5tprrzULLLBALPCpp54yI0eOjI+L7nSmA6uk1f392bNnm7322itThZtuusnMO++80TV5Z2AdPHiw2XHHHc0qq6xiunXrlij/vffeM7fddpu59NJLE88TCQEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQaHcClRxYXeU33nhj85Of/MTITJZp4Z133jH/8z//Yy655JK0SxriL774YrPkkkvGcWPGjDGPPfZYfFxmBwfW/1Ar48B6zz33mJNOOikSsuqqq5ozzjjDzDPPPLHQiRMnmhtuuCE+LrLjOpC6v5Ml449//GPsCFplBlb5Ddch9cMPPzQ77LBD1k83OLxmObCKDe+yyy6Ro3fPnj0TZX7xxRdmxowZ5qqrrqps44k/QCQEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKBGBNQcWK1Oiy66qNltt92MzNyZ5qz35ZdfmlmzZkUOgzLTZFIYNGhQ5Bxpz73xxhtm7733toelt64D6zPPPGNGjBhRSJamw2SzH+5sh05Jnz8D6+TJk82ECRPipP/sZz8z22yzTXwsjpgyS+6LL74Yx+Xd6Wx9J02aZHr37h0nV2YXfuutt+Jjd+e73/2uGTt2bByV5MC68847m80228wsvvji8XX+jjh133rrreaKK67wT3EMAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhDosgTUHVhdUuL8KLNYLr/88g2zdLrX3HfffeaEE05wo6J9d3ZQiTjttNPMnXfe2eG6ohHuLJtlnGJxYG10YBX+F110kenfv3+cFeKUueuuu8bHeXc624FVbGz11VePkztlyhQjdpgU/Gt9B9ZLL7001XFVnHwff/zxyGn1ySefTBJPHAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoEsTCOrAasnJrJbi0LjRRhuZXr162eho+/DDD5tjjjmmIW7ppZc2F1xwQRxX1iEyFuDsyBLtCy+8cBQjjoRbbbWVc7b5Lg6sHR1YF1lkETNx4kTTvXv3GOC0adMaZiiNT2TsdLYD64ABA8y5554bp/Dzzz83w4cPNy+88EIcJzvi5Hrqqac2OGX7Dqwyo2qfPn0a7pszZ47585//bK6++uqGeA4gAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAJzG4GWOLC6UNdZZx0jS6uvtNJKkQNgkgPrySefbNZaa634NpnhUxxHNYI/s+trr70Wze76zDPP5BKPA2tHB1YBt8kmm5gRI0Y0MDzrrLPM7bff3hCXddDZDqyStvHjx5tll102TqY4sYr93XLLLVHc0KFDzf7772+6desWXyM7aQ6scv+jjz4azbY6c+bMhns4gAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIzK0EWu7A6oLeb7/9zHPPPWdkqXYbZHbUK6+8Mp7d8uOPPzbbb7+9PV15K7NnyvLvSUGcDW2YMWOGGTNmjD2Mt9oOrEsttZQZN25cB4fIeeaZx/To0SP+XZkt9tNPP42P7Y4sQe/OYKudvg022MCMHj3a/pyZPDnZgVUuOOGEE8zaa68dXys899lnHzN79uw4ru76rrrqqub000/vkB+xAik7vgOrzNw6a9Ysc8MNN6TcQTQEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQGDuJdCpDqxJ2I888kiz/vrrx6euu+66aPbKOEJh57jjjjMyE2xWkGXjDzrooA6XaDuIDh482Bx//PEdfidvhDhJDhs2LL5cO31FHFglEddcc41ZcMEF4/S88cYbZu+9946P666vJHTgwIGRk/N8880Xp9vfEfvo3bu3WWSRRaJTvgOrfz3HEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCPyHQO0cWF0HzM8++8xss802/0mt4t6mm25qtt56a9O/f38z77zzxjO+2p946qmnzMiRI+1hvHXTJ0vCy0ybaeFXv/qVWWmllaLTMhup/J4f6uLQmZa+ddddt2GG16wZWEU3cf48++yzG3heccUVRhyRJdRd3yiRX/1bbLHFovxfYYUVYodcYfTaa6+Ze++910ycODHSqVevXtEtvqOulcMWAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgY4EauXAesABB5jNN988TuVf//pXc+6558bH7ECgswgsu+yy5sUXX2z4edeZefr06Wb06NEN5zmAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAIJlArRxYb7rppmg2VEnqF198YbbaaqvkVBMLgU4msNRSS5kLL7wwTsVtt91mzjnnnPiYHQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAATSCdTGgXX77bc3e+21V5zSe+65x5x00knxMTsQqBOBU045xay55ppxksaNG2emTp0aH7MDAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQDqB2jiwShKHDBkSp/Tuu++O99mBQCsJ3HDDDWbmzJnmmmuuMQ899FCHnx42bJgZOnRoHP/222+b3XbbLT5mBwIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIFsArVyYM1OKmch0BoCN998c/xDn3zyiZkzZ46ZPXu2WWihhUy/fv1Mz5494/OyM3bsWDNt2rSGOA4gAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQSCeAA2s6G87MpQRcB9YsBF9++aW55ZZbzIQJE7Iu4xwEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACHgEcWD0gHEJg/Pjxpn///qZ79+6pMGRW1hNPPNE888wzqddwAgIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIFkAjiwJnMhFgJmwIABZuDAgaZv376mT58+Zvbs2Wb69OnmkUcegQ4EIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACFQjgwFoBHrdCAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIFCeAA2txZtwBAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgUIEADqwV4HErBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQHECOLAWZ8YdEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhUI4MBaAR63QgACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCBQngANrcWbcAQEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIFCBAA6sFeBxKwQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEBxAjiwFmfGHRCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIVCODAWgEet0IAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQgUJ4ADa3Fm3AEBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBQgQAOrBXgcSsEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAcQI4sBZnxh0QgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACFQjgwFoBHrdCAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIFCeAA2txZtwBAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgUIEADqwV4HErBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQHECOLAWZ8YdEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhUI4MBaAR63QgACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCBQngANrcWbcAQEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIFCBAA6sFeBxKwQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEBxAjiwFmfGHRCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIVCODAWgEet0IAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQgUJ4ADa3Fm3AEBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBQgQAOrBXgcSsEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAcQI4sBZnxh0QgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACFQjgwFoBHrdCAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIFCeAA2txZtwBAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgUIEADqwV4HErBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQHECOLAWZ8YdEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhUI4MBaAR63QgACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCBQngANrcWbcAQEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIFCBAA6sFeBxKwQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEBxAjiwFmfGHRCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIVCODAWgEet0IAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQgUJ4ADa3Fm3AEBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBQgQAOrBXgcSsEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAcQI4sBZnxh0QgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACFQjgwFoBHrdCAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIFCeAA2txZtwBAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgUIEADqwV4HErBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQHECOLAWZ8YdEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhUI4MBaAR63QgACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCBQngANrcWbcAQEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIFCBAA6sFeBxKwQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEBxAjiwFmfGHRCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIVCODAWgEet0IAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQgUJ4ADa3Fm3AEBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBQgQAOrBXgcSsEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAcQJd3oF1wIAB5qSTTsokM2HCBHPnnXdmXmNPasuzctnWgwD5W498IBUQKEPgnHPOMfPPP39065QpU8x1111XRgz3QAACEIAABCAAAQhAAALKBM4666xY4vXXX2/uueee+JgdCECgkcDRRx9tlltuuSjyySefNGeffXbjBRzVjgDjibXLEtUEkb+qOLu8sEGDBpkRI0bEel544YXmgQceiI/ZqQ+BEP3ToUOHmtVWW80stdRSplevXg3KHnHEEebtt99uiOMAAu1CgP5pu+TUf9JZ9/dlc1v/am7Tt5X9oRDt+X9KEnsQgAAEINBKApUcWI877jjTp0+fKL2fffaZGTVqVGbal1hiCTNmzJj4mueee86ce+658XGIndVXX92cdtppmaKvvvpqM2nSpMxr7ElteVZu6K0w6NGjR/Qz8pB8/PHHh/7JSP7CCy9sjj32WPONb3wjOr7vvvtys3YTKLYlD/02DB8+3O6qbts1f1UhIKytCQwZMsTssMMOajqcf/755plnnlGTF1LQH//4R9OtW7foJ2bOnGnK1BMHH3yw6du3b0My5QX7Y4891hDnHyy77LJmn332iaOffvppc9VVV8XH7EAAAhCAAAS6IoF2eB7sitzR6T8EDj/8cLP00kv/JyLn3oMPPmiuuOKKnFdzmQaBm2++ORYjzqvNPjSOL2YHAnMhAamf7HjrBx98YHbccce2prDddtuZTTbZJNJJnHnsc/snn3xi3nzzzag+vuuuu1qq4zHHHGMWW2yx1N+Uce5Zs2ZF4yEyHvDSSy+lXisnGE/MxNP2J8nfts/CliqwwQYbmNGjR8e/OXnyZCMTqBDqR0Czfyr1xFFHHWV69+6dqqg8u8iHKQQItCOBrtY/bcc8KJpmjfdlRX+zyPVzW/9qbtO3lf0hzfa8iA1zLQQgAAEI6BOo5MD6+9//3nTv3j1K1Zdffmm23HLLzBQOHDjQjBs3Lr5m9uzZZq+99oqPQ+xodwi05YXQOUmm23h/+umnZtttt026TD1OXiZecMEFsdxXXnnF7LfffvFx3p0bb7zRzDfffNHlX3zxhdlqq63y3lrounbN30JKcnFMYPz48bFdTZs2zVx00UXxuXbdEQfMH/7wh2rJF+f3vDNUq/1oSUEaD+Ruu2aT8eqrr5p9993XHiZuf/azn5ltttkmPpfnnvhidiCQg0BXrK9yqM0lEOgSBLpy+XXbzbo+D3YJI0KJVAK/+93vTM+ePVPPp52Qj40OO+ywtNPEByDgjknUxYG1K9fPAbKw7US2c/52FQeBAw88MHJcteN5WUYkzqLDhg3LukT1nNuHySNYnvHPOOMMI+1HUmA8MYlK14lrt/xt5/qvK1hNKx02ugKvztRBs396ww03xBPIpOmEA2saGeLbgUBX6Z+2A2utNGq8L9NKS5KcdutfJelQJG5u07eV/SHN9rxInnItBCAAAQjoE+jyDqyCzJ9Nb4011jAjR46MaRaZgTWEvDghAXfcxruVDqyi0p/+9CczzzzzRNq9//77ZqeddiqsqZv+d955x+y6666FZeS9Qdte8v4u17WegGubM2bMaDqLdOtTWPwXcWCtNgNr2kssmTlh+vTpqRmCA2sqGk4oEeiK9ZUSGsRAoPYEunL5ddtNHFhrb4pdMoE4sLZPtrrP9HVxYO3K9XP7WEa4lLZz/nYFBwFZheuSSy4plMFPPfVUw3htoZsLXuz2YYrcetNNN6XqxXhiEZLtd2075W8713/tZxkdU9xKh42Ov05MEQJa/dP999+/YXIfmcX73nvvNTKZy+effx4n6be//W28zw4E2o1AV+ifthvzqumtuwOr6NdO/auq+TG36dvK/pBWe66Rx8iAAAQgAIFqBOYKB1Yf0Xe/+10zduzYOLqoA2t849c72vJ8+RrHbuPdagfW6667zsgSYRLkgX3rrbcupJI/6P3cc8+ZQw45pJCMKhe3Q/5W0W9uvrcrDujKTNhZZWzhhRdu+BpcliMUx/K0cNZZZ5knnngi7XSt4jUeyNNeYr3wwgvmoIMOStUXB9ZUNJxQItAV6yslNIiBQO0JdOXy67abOLDW3hS7ZAJPPfXUDi88FllkkXiVBVFann/feuutBv3vv/9+lnJtIBL+4PLLL49X8JkyZYqZOHFi+B9t8gtduX5uovpccbqd87crOAj4Y3lvv/22EQfVRx991MyZM8esttpq0eox/uyssnLX1KlTg9uo24eRH3v++efj3/zGN75hZOxkwQUXNN26/d9HsvHJr3byzqDHeKJLrevt1zl/27n+6wqW0kqHja7AqzN10OqfnnfeeWb55ZePVZFx4tdffz0+ZgcCXYFAV+ifdoV8KKKDxvuyIr+ncW2d+1ca+vkyurK+rewPabXnfv5wDAEIQAACrSeAA+tXzHFgDWt4F1xwgVl66aXjH9liiy3i/Tw7Q4YMMUcddVR8qSxnLsuatyp05Q5kqxjW9XfmxgHdX/ziF+Z73/tenCVV679YUA12NB7I/ZdYrlriOC8O9EkBB9YkKsRpEpgb6ytNfsiCQGcS6Mrl1203cWDtTCvjt10Cv/zlL823vvWtOEocJWVJTwIEfAJduX72dZ0bj9s5f7uCg4B1YJWPZi+66KJUp1T3o3ex01bN0Jy3D3PEEUeYDTfcsKEIvfvuu2aXXXZpiEs6YDwxiUrXiatz/rZz/dcVLKSVDhtdgVdX0GHSpEmmd+/ekSqhVw/sCrzQoT0JdIX+aXuSL59qjfdl5X+93J117l+V0yj7rq6sL/2h7LznLAQgAAEIJBPAgfUrLlUduNqhg9GZM7D6DnMjRowwzzzzTLJFJsTuuOOOZvfdd4/PXHbZZUaWimxVaIf8bRWLrvY7c+OArl8eq9Z/dbIJjQdy9yWWr1vWcoY4sPq0ONYmMDfWV9oMkQeBziLQlcuv227iwNpZFsbv+gRwYPWJcJxGoCvXz2k6z03x7Zy/XcVB4Ic//KG59dZbM83uRz/6kTnwwAPja2TGOnm+Dh2K9GGGDRtmhg4d2pCkXXfd1YiTUlZgPDGLTvufq3P+tnP91/6WYQwOG10hF4vpcNNNN5l55503uunll182+++/fzEBXA2BNiDQVfqnbYBaLYka78vUEpNTUJ37VzlVKHRZV9aX/lAhU+BiCEAAAhD4mkDtHVgHDRpkpAFfddVVzTLLLGO++OIL89JLL5np06ebu+66y8gDYdGg3SHQlCcDt6uvvroZMGCA6dWrl5Eltp599tloia2//e1vRVWNr+9MB9Y999zT/OQnP4nT8qtf/crIkoF5w2GHHWb+67/+K758zJgx5rHHHouP3Z12sBeZhWLttdc2q6yySrS0zGKLLWY++ugj88Ybb5iZM2eaSy+91FUpc7+svSy77LLxLJwffvhhNMPFbrvtZr7zne+Yzz//3DzwwAPRLEWzZ882Sy65pNlmm22icihLpz399NPmqquuisphZuK+OhkiP5r9Ztr57bbbrsOpn/70p3Gc8J88eXJ87O7885//NLLMaLNQNj+aydU8r+nAGiJ/q5SPtAdyqYMGDx4cfQkvbcisWbPMww8/nDgTl/sSS7jLkrPukoZpS0CVcWAtay9rrrmmWXnllWOzyDujmFvu5ea0+/r27Ru9nJN2qE+fPhG3f//73+aVV16JPj6Q+jdPeYgTGHCnir1suummpkePHlHqHnzwQfPqq6+mplSYCz8JUkc2ewkr18mMPFLPC89vfvObRhjKTD3Srgu/P/zhD3JZYqhzfeXaUbu0Hy5P6X/YF81SL0j7tvjii0d9rvfee8+89tprRmaBeuKJJxLzJlRkFXuRNGnbs8tM2n0p95L3G220UdQfkL6L1AmyBOzf//538+KLL2ai0Zbn/1jZ+tSXI8duWsvYi3u/la/d37By67B1281QDqxF81eWj3efHWQmtzLPjPJMJs+gEmSp49tvv10Veej6tGp7Ln0Naf9sKMLRLQePPPJI9Ixj5bRiW0cH1qr5kcatavvhy9WSt9Zaa5lFF13UFx8fy1LdL7zwQnycd6dK+ly7tL9Xl/q5VeWtaH1qOdmtdn/DytXYtiJ/q/Jz9ZQyst5665nVVlstKisyznnfffeZO+64w7z55pumiINA1fqlVfbn6u/ur7POOua4446Lo2RsZu+9946PQ+0U7cPI87N9fpQ0ycpQskJUVtAcL9Yaf6lqL2n6Vqmfk2Rq6ZskWyuuLvnbivqvLLPQ/V2brrrZS1GHDRmXkDF3G/zxui233DIem5S2wj5/FxnvtLKTtkXbt3Z53vJ11eqfuvlhf0PywuahjDndeOON9lS8/fjjj1PfPcQXVdhpVXte1F58lercn5S0uvkborxp11ea8jT7p36+Vz3W6r9o5W+ryltVbhrvy7LSoGl/9neq9K/cPpHGeLZNU8htFX39dFWtn7XlafeHbPpCtOfa9b2sCiWrC8vYsszQLu+k5Z2g+PnI8770kVdcccVIJXkn1Yr3rdrtb13LW6vq56rlTTs/bPmQrZs3Zd5vubK097Xa86R0VfEXCC2vbvai1R9K4qYRV2sH1vHjx8cOI0nKykvSa6+9NnKmSzqfFqfZIZDf0JAnDfnIkSNN9+7d05Jt3n//fXPUUUelLmGdeuNXJzrTgVUaaUm3DTIII8s42iDLcdmXwxInToTuQI0MCksjb8MWW2xhdxu27WAvhx56qNlss80a0u0fiF0/+eSTZtSoUf6p+LiqvciLOtuAiTOWONAutNBCsXzZkU7TQQcdFC315g7Syzlx6pMZMrIcvkLlh/x+meCWgaL3y8tVYZEWquZHmtwQ8VoOrCHyt2r58B/Ipe6QdNov4H2e4ny13377NUS7L7HkhAw4brvttvE14vh6zDHHxMd2p4gDa1V78X9L6swJEybYpKRux40bZwYOHBifP/bYY81DDz0UH0udIH/ibNksSJ0h9XqR2bSbySx6Xttehg8fnpoE+fBipZVWis43c9CSQeutt9461e7cH5EPcU499VQ3Ktqvc33Vbu3HUkstZS688MKY8dSpU42UBflYRBxX04I4Nbsv0tOuqxqvYS+SBr/+q2rPrg2Kk+rjjz+euUyqzIwvM+SnBW159neq1qdWjt1q2Iurq5Wbd9usv5FXTiuvc9vNZvWjpEvaISmDNsjHUnvttZc9bNiWzV8ZBJT23wZxBjrzzDPtYe6ttK3yAaUE+QBmq622yn1vngtD1ada7fmPf/xjs++++8aqyAeko0ePjo/TdmRGup133jk+LYPAJ5xwQnzcip06ObBq5YfPTav9sHK15bl1g/0Nd1t0iXCN9NW5fg5d3srWp26eyb52f8OXX+U4ZP5q8bP6+XWUjZettKVy/oADDog+JpS4Dz74wMjKRH7Qql9C25+fbv9Y2hZhbEPaM5I9r7V166k8fZgLLrjALL300vHPywyX7nNGfMLZ0RgvFnEa4y9a9uKoF+1q1M++TA19fZkhjuuSvyHrv6rcQvV33XTV0V6kTnP7zVnjdr/5zW9Mv379YpXEyVHG3GU2ahv89rfMeKeV5W7Ltm/t8rzl6ir7br3vn5PjvP1TNz+S5GTFFNsGLgAAQABJREFUyUQt4lQVIoRuz8vai6+ry08mc6k6fuXLr3rsp0+rvEm6tOsrTXla/dOq/P37tfsvWvkburz5HMoea+mb9Pua9ufKr9K/cvtEGuPZbrpC7VfR16ZJq37WlqfdH7LpC9GeS3ukVd+ffPLJRpxsk4I8d8o7emn7ZPIgCWnP+0n3V4nz64Oq7W9dy1vo+lmrvGnnh7UNjfdbVpbmVrs999NW1V8glLy62otvf1r1n8+x7HEtHVglM+VhLs3pyFe22YOOf71Gh8CVWVXeiBEjzCabbOKKTN2Xxu3iiy+OXhykXpRwwm1IxPnQdchKuFw1Sr4umTRpUizTHwwQBzF3hkP/5f0ll1xixGtewmeffRbNlhYL+2qnHexFHJ/kxa2wyBvkhYX9otq9R8Ne3AFEV3aR/bSX2KHzo0ga3WvdMuDG59n3bdK9RyM/XHmh96s6sIbIX63y4Ta4MtOazPrUs2fPTKTihO06Z/gPPzLgLI6atuxKHbz77rtHs2i6gn2nUl+uvVbDXvw6VWb0lBmUmwWXzyeffBI7sdv7zjrrrGjGUHvcbCss5GOEpFkFmt1b5XwIe2nWj8jrwCozBRVpX9PqljrXV+3WfvgPbPJCXGYbX2CBBTLNsBUzP2nZiyjilm8Ne3ZtUOoL/0OWJHjTpk0zY8eOTTrV8CGVhjz5EY361E+shr247Hz5zY7T6oRm93XmebfdlHZBvt7MCnkdWKvmr5uutDY5K51yzl0GMkSdEKo+1WzPXY55nXjdWQOFowyISr3UyuC/fJP+ivuBZCvTopkfNt2a7YfI1JYnMl3bkWM/+GMC/nn3WCt9da+fXWaa5a1qfermhXZ/w5VddT9U/mryEx3d8a00naU9ldUb7AfuaS+0NOuXUPaXpqON9z94l3gZOG82s6m9v8rW1TlPH+a8886LVlGyv3nbbbeZc845xx4mbquOF2uOv2jai1VWq3628jT1tTJDbuuSv6HqPw12ofq7krY624ukLY8Dq3wEKqub2CCr3Mj7APnIzw1u+1t2vNOVJ/tV2ze3Dq3r85avs5tm/5wc5+2fuvmRJCcr7vDDD48mLsm6pso5V0f6k+VIuvmrVd606ytteZr903LU0+/S7r9o5m+o8pZOo/gZTX3tr2vbn5Vrt1X6V26fSGv82aYr1LaKvpKmqu25r5emPO3+kE2rW/ZsnLst055r1ffivyOr2mYFmVRM2mjrF5P2vJ8lo8w5tz6o4/ujMjql3ePayNzWH9J4v5XGtUq8dntu06LlLxBCnmZ9ql1+XXla9Z9lqLGtnQOrOCrKl6fzzDNPrJ9ULk899VQ086g4I8mLT/nS0w3+rJ7uOX+/aodAU56fFpEtX9rKDJwyjbhUNFL47MC1nM9b2cq1Nrgdt1Y7sEoa3ILw7LPPGvGElyBLzlx55ZXRvv3nO1bJUr69evWKTsvynXvssYe9NHJsbQd7uf766838888fp1t2RBdZml6cFWTK7OWWWy7Kb7vcTJIDq5a9+AOIdklscYxbf/31G9IpBzJzkgxGyRTX1rHczye5rhXlV36nTJCGwn7RZO8XJyYbZJBQ6pmkIEsnix36QSs/fLkhj6s4sIbKX63y4dYzLkNxOpF6R8qWzOa84IILuqejhzw7k6jbsZWLZMBZyqaURxv+8Y9/RLPi2GPZ5nFg1bSXiy66yPTv3z9Owv7775+5PLI4FMk1NiTNauN2IKWdkaXUZcYHWd5C6gj5PSkzviPbIYccUmpmcJuWotsQ9qLxwCZ6uG2tHMuyYffee2/EUvovYksym5/NuzRntTrXV+3WfvgPbGLbtp2VPJIPY6Q9ljZA2giZgVj6oCGc1eT33KBlLyLTrf807NlPm023zFw9Y8aMqB6Q+lT6cW6QeiRpmXVteZr1qZt+DXsJUX7dNNZt32038zh/5HFg1chfd/ZUacNkZuwiQfrm7ooRssTTGWecUURE02tD1aea7fnxxx9vBg8eHOsiL9llxuW0IDPTyQx1Nrz11lvRhz/2uFXbujqwavWv/Dq1bH/D5oe2PJErH2+6K3xI2yvjGjbkfaEg12ulr+71c4jyplGf2jyTrXZ/w5VddT9E/mrzk9WHNtxwwwZV3b7VmmuuGX846V6U9kKr3et7Wdrv9NNPbxjvfOmll8zPf/5zV/1g+0X7MFdffXVDvXb++eebP//5z5np821IZLgf+GfdrD3+omkvNt1a9bPI09bXpjHkti75G6L+0+IWqr9bd3vJ47Ah70Pc5+l3333XDBs2LBpH8vm77a97rsh4p3ufb7tyruj7qHZ43nJ1ln2t/umRRx7Z4aPob3/72/H7THln8sQTT/g/Hx2fe+655l//+lfiOY1I+pPVKWqXN+36Sluedv+0eg40StDuv2jmb4jy1qh99SNNfSU12vaXpKHfRhXpP/t9UyvffeYqMp5t7w+5raKvf6+ks2h77uqmLU+7P2TTqtWea5cPWWV54403tsmMttJXs32CNdZYo+HDJXth2vO+Pa+1dfWt4/sjLT1FToj6Wbt8aOeH5afxfsvK0txqt+c2bVr+Atry6m4vrv1Z3WVb9vnSlaGxr+bAKomRl5bNguuYmrRkpDvLmcgSUGPGjGlYNkXiZcZScYK0TgjyMkhmxZOXN82CbzRFOkBJsqvI85eJEYehffbZp+FnpFMoX/q7DpBJzkcNN3kHbsetMxxYZYDWzmDoOqEOHTo0GpzxkhvNJiizCkpwC5E4TYwaNSq+vB3sRRzfNt988zjNUk7k5W/SLEDCSBwMZSA/yYFVy178AUQZNLv22mujNLqNiESIU6d0vCRss802kaNedPDVP3Hac5c0akV+2N/W2MqSb7ZO8m0rj3yt/MjzW1rXVHFgDZG/muXDrSuEV1pZ82dOkeVEZJZVCe5LLDkWB1aZbVjKh52xUdobfxnhPA6smvYi0+1LObZhypQpRvInLfhLuiQ5nZ544olR3fOXv/ylwWnHlynXuc7f4uAt7XQrQih70Xhgk1lwd9pppxhDFhdp12XmX3HskRefeUJd6qt2az/8BzbLWhzaZMYk39lSZj+RwVtxdBd7CxW07cWt/zTs2e03WgZJH4v5S+JIv11mYvKDtjzN+tRNayh7qVp+3TTWbd9vNzWeBzXyV5bdlA+vbGj2oYe9zm795YZCzAYXqj7VbM8HDBhg5CWrDWll3J6XfpMMUNtwzTXXmKuuusoetmxbJwdWzfwQgNrth7a8rEx224K8Dqyh01en+jlEedOoT9081e5vuLJD7FfNX21+fpspA+2XX355g+qnnHKKEUdWN6S90NKsX0LYn6uDvz9o0CAjurof66fp6d+rdezmh/RfsmaRlxeRdlzM/r442orDbVaoMl6sPf6iaS+is3b9rK1vVr5onatT/vo6Va3/fHllj0P1d+tuL1kOGzL2L86f8vGsDc0++nLbX7mnzHin/S3ZarRv7fC85eqctl+mf5oky129o5UfY/hpCdGea9iLm07XnjXGr1zZGvtu+kRe1fKmXV9py3P7Q6Jv1f6pyNAM2v0XzfwNUd402YksTX1Fnrb9iUw/VOlfuXW6lVtlPNvKCLmtoq92/awtT7s/lJUPbt7nHW/SLh9+fXrLLbcYeS/rBnkPLauQuKFVz8Guvhrtr8vc6lOX8haiftYuH9r5YfMg1PstK7/sVrs9l3Ro+gtoy6u7vbj2J7pX7e+KDM2g6sBaNGG+A6vM7iJe8TbIlyLbb7+9Peyw9QfL7r77biMv0puFKh2CJNll5YkDkBRYG2QWMHEQTAoy5bhMPW5DkgOVPZe0dRuSpJkzk+7RjJMXn9JgSHB//5hjjjHrrrtuh59yZzJw037rrbeaX//619H17WAv4ghz6aWXxo7WknCx8fvvv7+Dzm6EdGDEnt2gaS/+AKI4UskMqxJkhtsddtgh/mnXwVucrmRJERuOO+448+CDD0aHrcoP+9sa2yoDupr5oaFLXhllHVhD5K9m+RD9/QY37UHF78C5zhj+Q4Z1YJUy4c7+PHXqVDNu3LgYezMH1hD24trv+++/3+A8GSfs6x33WpnVYZdddvEvKXR84403xktcZLVdhYQ2uTikvWg8sMnS6dIfsEE+thDHeK3g5mFRh3tN+2u39sMv75IfYrPS7vnL8mnlVR452vbi1n8a9uz2vUQfceSX+jAp/Pa3v214+SYDMfJhgBs05Wnas5tG2Q9lL1XKr5/Guh377WbR9PnPg1r568tpNnOon26/v7TFFlv4l1Q+DlGfFk1Unvb8iiuuaFjFwP+Azf1NGai0M7U3cwZy79Per5MDaxHd8uSHdvuhLS9LX7ctSOun+/eHTl/d6mfN8ubXg1l99rzjTdr9DT+/tY+r5K82P39gXWZhkY+mkoLft9J4oZWnftG0vyS9bJysqCVjhK7zqoz/ikNoK/vnbh8mq836/ve/b2S2PbsakeiRd3b3suPFIcZfLP882zz2olk/d7a+eZgkXVPn/K1S/yXpWjYuRH+3HewlzWFj0UUXjRwZ7AfywtUdk0zj7La/ck1aP8p/nk2SrdW++XLq+LyVxtONL9M/de+3+3VxYJX0aLbnfj7PDf1JzfKmXV9py+vs/qktP1rbPP0XzfzVLm9aHFw5mvpq25+bTne/bP9KZLh1uhxXHc8WGaFDWX2162dtecJNuz+UlRdu3qf1k/z7NcuHX59mvZ/x22mN531ft6RjV9+s9Mm9rrN62rOyy1zuqVt58zlXGc8OUT6080PyQIL/PCBxdXgfKunIG/K059r+Apry2sFeXPuTfEmrN317Snq+zJuvRa6rlQOrP/NjnpluZAlDWZZXQl5oZTsEaWDLyjv88MPNRhttFIttNquqv2S0zBx22223xfdn7bgDV7JMruugmHWf1jl3Nh63sRPnzsUXXzz6Gfna2C6dY5follkZ3OU6XcfWdrAXmYVs2223jTG+/PLLDct4xydy7Gjaiz+A6L6Ul9mNZfkpG8Qp3HWmdTslkgd29rpW5YdNl8bWLRdFHcI080NDl7wyfIcM10E5S0aI/NUsH5J2v8Hdc889zZtvvpmoluhtlzV160T3JZbcaB1YZd91yvBfWDVzYA1hL+5DhKRv+PDhRh48/LDjjjs2LN+b9OWff0+zY38WW7cOaXZv2fMh7UXjgU0+TpCBHBukky1tnFaoS33Vbu2H38GW/NDOmzJ5rG0vbv2nYc9uWy/6HX300eaRRx5JVFVmtnRnrErqz2rKC1GfWsVC2UuV8mvTVtet324WTafvwKqZvy73hx9+2MiHczaIM8phhx0WHcqHgTvvvLM9FW3dQS5Z4WPXXXdtOK9xEKI+LZquPO25sHH1v+OOO8yZZ57Z4afkAzw7o72cLNq37iCwQkS7OrDmyQ/t9kNbXla2uW1B2sCYf3/o9Ln1RGfarNVbs7xp1qc2fdr9DSs31LZK/mrzc8e+RF/3Y2Bff/+5R+OFVp76RdP+fJ3cY5mdW1Y7sEGex+WFXyudV+W3/T6MOA7bIOOUksbll1/e9OvXz0bH24kTJyauqhRf8PVO2fHiEOMvftqyjvPYi2b93Nn6ZrHIOlfn/K1S/2XpXPRciP5uO9hLksOGjEPIew37HktYypLG++23X1OsbvsrF5cZ77Q/otm+uXZWx+ctq3PWtkz/NElenRxYNdtzTXux3Fx71hi/snK1tm76RGaV8qZdX2nL6+z+qVaeWTl5+i+a+Su/q1nerB6aW019te0vTc+y/SuR59bpclx1PFtkhA5l9dWun7XlCTft/lBWXrh5n3e8SbN8+PWpPCulTWTm1xsaz/tZbOw5V1+N9tdlLr9Rt/Lmc64ynh2ifGjnh83nUO+3rPxWbPO05/64WRX/K9FJU1472Itrf6J/lf6u3K8dVB1Y3cG+pIQus8wyDUsK+i8sZRlz68Ao98uXm+LsaINd6luOu3XrFkXLMs4LL7xwtO87FEWRCf/KdggSREVRZeXJi79VV101FpvmfGQv8GcAlAdjdzZMe13S1h2YbbYsTdL9VeP85a732muvaHDaLSAyw6zMiCbBOiPL0p+yJI0NBx10kHnhhReiw3awF5lhVzztbZDje++91x4W2mraizuA6M/m67989mdScwenJM/+8Ic/RHq0Kj8KQWtysatL0ReWmvnRJJmqp8s6sIbIX83yIZDc+qTZDN7u9O1u2+HWlSLTdWB1y42cE/u58MILZdc0c2ANYS+bbrpp5LQaJeCrf0lOY3JOyqnMqmSDrX/tcdZWZs5ebrnlopd2bhssH1/0798/vrUVDqwh7UXjgU06eD/5yU9iJjLbuDjQ2Fmq4xMld+pSX7nloB3aD/+BzS3vJbNC5TZte3HrPw17dgcg3Jnzk5SXvru0ETY8/fTTsWOgjdOUF6I+tekMZS9Vyq9NW123frtZ9XlQM39lWWaZ5UjC22+/HS11azn6/SFZxnPy5Mn2dNTG23ZP6nFxNNIOIerTtDRWbc/dOiatTvBfZmQ5Z6WlUyu+7g6sVfJDu/3QlpeVh25bkPeFQuj01bF+1ipvmvWpzVc3bRr9DSs31LZK/mrzcz++//TTTxs+dE7S3y0vRV5oValfJB1uHoeo72UlEHc1EPkN+Riq1c6roqvfh5G4POH55583Bx98cJ5LoxU6ZKZSG/J+QBxi/MWmwd1WsRfN+rlV+rq6a+yXfR/QCn2r1H8abKyMEP3dVvCz6S+79R025F3At7/97XilApEr7zfkPUee4NbNZcc77e9otm91f96yOmdt3fY2b/80SV6dHFglfa7NVGnPNe3FcnPTVsf+pJu+quVNu77Slteq/qnNe61tlf6LZv5afVyZVcqblae5ddNWN3tO07Ns/0rkuXV6Wl7Y380znm2vDbktq692/awtT5hp94ey8sHN+7ztuWb5cCc/Cvm8n8Wg2TlXX43212Ve1/Lm6pyWxjzj2SHKh5s2jfyw+R/q/ZaVr7mt0p5r+wtoymsHe3Htr2r/QNMmrCw1B1Z3Vk0r3N8OHDiwYcll34G17OCh+zsygJY24569rmyHwN7vb8vK87/IaOYAJDMFHXvssfHPT5s2zbgDofGJhB33YSTv170JYkpHrbnmmuaUU06J75d0S/7LsmESbOGwDZ51ipGvkMVJ2QaXUTvYi+845qbf6pR3q2kv7gCi78yz1lprGZl11Qbfsdqt1GTmjKuuuiq6tFX5YdOlsa0yoKuZHxq65JXhO2zkfYESIn81y4fo79qmdYJP4+LOXuq2X76ergOryHIHJN0HkWYOrKHsxU2vrUd9nW29KvHNuMg18mXa1ltvbdwlzSQ+K+y+++5GPo4IGULai8YDgiyFKbNpWIcny+Kjjz4y8pLzoYceMvfdd1/iLLn22qxtXeqrdms//Ac2v++ZxTzkOW17ces/DXt26405c+aYPfbYIxOHa59JjDXlhapPRcFQ9uLyKfrBTCb4Gpx02yG3PU1LWrPnQc38lY+w5MMsCX7a/Nnf3GWOVlttNXP66afHKsjHKpKH2iFEfeqmUbM9l+c4eZ6zwV+hQeJdO3dnt7f3tHJbRwdWrfzQbj+05WXls9sW5H2hEDp9rt3WpX7WKm+a9anNV+3+hpUbalslf7X5uWnxP6pI0t9l3cyBVat+kXRo2V+SThInK22tvvrq8WkZn3jggQfi41buuH2YPL8rY2e33nqrkY9e8oay48VF05aUnrTxcS170ayfQ+qbxEYrro75a3Vz65zObN9C9HfbwV58hw2bL+5W8sh+FO/GJ+27bUKzcb208U4rV7N9q/vzltU5a1umf5okzx0vfumll8zPf/7zpMtaFqfVnmvai1XetWeN8SsrV2vrpq9qedOur7TluW2Fdv9UKz+sHK3+i2b+2rRplTcrT3Orqa+2/aXpWbZ/JfLcOl1jPDstjZrxZfXVrp+15Qkj7f5QFnc37/OON2mWj6L1qVuemj3vZ+ld5Jyrr0b76zKva3nTqp9DlA/t/LC2EOr9lpVfdavVnmv7C2jKawd7ce2van+3qk0k3V8rB1a3sktKbJ44WXbqxRdfzLy0bIcgTWhZea5TqXXYTPsNiZfBwfHjx8eXFPlSV5wM7Uy1zRqm+AeUd9z8ldl155tvvnjmhaeeesqMHDnSXHvttbHT1CGHHBIte/29730vSonvnOXKK5vU0PZSNI+z9CgqK8te3AHEzz77zGyzzTbxT8uswPJ1gA0+I7djJWmSvJTQqvyIfkzpn9upLDqgq5kfSurkElPWgTVE/hZl2ExBt8F97rnnjNQhaSFtQNe1b7nXd2CVGaFlZmgbrANwMwfWorpmlV/727IVBxJxOrdhzJgx5rHHHrOHxi3rEmnTG1/g7MjXTmeccUbDjBDO6cxd+Z1//etfmddUPVmUYbPfc+2lWbuYZi/+b0h9ufnmm/vRDcdS5z7xxBNGZBZhVpf6yrWpdmg//Ac2299oyJROOtC0F217dut86VdLWrOC+7LG76/JfZryitYFeetTSWcoe6lSfiVddQ5uu+k7iSalu5kDq2b+brLJJmbEiBFxMuxSRjIrq8wW5Ab3K2y3npNrdt11V/POO++4l6vsu7+jVZ9KwkK05+JsJE5HNvgzLe+4447Rc5s9f8sttzQ8t9r4Vm3r5MAaIj802w/JE215afnstgV5XyiETl8d62et8qZZn9o81e5vWLmhtlXyV5Nf7969zaRJk2I18zi2uDO4pL3QClG/aNlfrKy3484clmcc1Ltd9dDtw4hgcVB1g3yw+u6770bPbDL+mtfRzJVRdrzYrS9deUX2pW53x8dD2ItW+xFC3yKsyl5bp/z1dahS//myqhyH6O+2g73kcdgQrv6YYxprt/0tO95pZWu2b3V/3rI6Z21deyrSP/VlumMiedp5/37tY632XNNerI6uPWuNx1rZGls3fVXLm2tfZdPmtuea8kL1T8vqmXafdv9FM39tmrXKm5WnudXUV9P+snQs278SmW4aNcazs9Kpda6svtr1s7Y84aPdH8pi7uZ93vZcq3z448yzZs0yw4YNy0qucXmnPe9nCihx0tVXo/11mde1vGnVz25+5RnHyPM+Sjs/rEmEer9l5ZfdarfnRfOkWbo15RWV1Rn24tpf1f5uM7ZlztfGgdXPHHmJ9/jjjxfWSRzvmr1gLNshSEtMWXnuYGmeF77y+26D0Mwj2k2vu1z2ww8/bI455hj3dEv23Qf5KVOmmCWWWCKeyef666+PXiS7szHIEqQya9Lyyy8fpe/VV181++67b7TfLvbi5rH7YrwMcFdWVXspMoAoXwzLwIsNbjqkEhYH1lbmh02HxrbKgK7LoWp+aOiSV0YZB9ZQ+esyrFo+RH+3wS3bAXfTJDKTBpPda6yjVjMHVvceTXvx2x+Z4fOEE06QpEfBXc4r63dl0Eq+CurRo4e9NdqKfjKruXyFLbOp2SCO7gsttJA9jBxlizhjxjcW2HEZ1sVekpI/dOhQs9dee5n5558/6XQcJy9ITz31VCMP1HlCXeqrdms//Ae2O++8s8EJKw/7kNdo2YtG/efq6fY38zj93njjjdGHSSLDn9ld4jTluXVBVr0mv2uD+/tZ/edQ9lKl/Fod6rotmh/NHFiLyhMuWfnrnpN8EOcTd/liGXTq1q1bhFc+fJEHdnfpINvOh+CvXZ9KGkO25+J8JfIl+GXP/7I4lNNv9OM5/tXFgTVkfmi1Hxantjwr19265THvCwV7f6j01bV+1ihv2vWp5IV2f8Pmb6htlfzV5CdjWuedd16sZp4XWu5M4UkvtELWLxr2Fyvr7bjjguIcKm1yZ4UyeVw0rf7zetYHpVZ2iPGXkPZStX4Ooa9lGXpbl/xN0rNK/Zckr2ycdn+3XewlzWFDVuhxx4ryPmtotr9l6j63D+c/T7vn6va8lcdu3fQX7Z+68t32rQ4OrJI2jfZc214kXZr2LPK0g1b6tOsrbXkh+qfaeRGi/6KVv76uGuXNl6lxrKWvtv1l6Vamf2XluXW6xni2lRtyW1Zf7fpZW54w0+4PZeWDm/d52/NQ5UNWYzz44IOzktvlHFjrXN406ucQ5UPL/nxDC/V+y/+dIsch2nM3TzT8BTTlubL8dyhp3Nw6zH/eknu07UVbXppeZeNr48AqCriZ895770XLGJdVLOu+sh2CNJll5bkD0nkMeKWVVopmbLPpeOaZZxpmFbLxdd1eccUVpk+fPlHyZIZA6fRKpSXBLlPvvlAWB+all146njlWll8+9thjo+vlXzvYS9E8jpVL2CkqK8tetAcQW5kfCWhKR1UZ0NXMj9IKlLixjAOr/EyI8laUYTN1NRpct2Mhv5fkwHrEEUeYDTfcME6OTC2/2GKLNcxk7Drcy4VFdc0qv/EPf73jzs4js8Rsu+220Zm+ffuaiRMnxpdnfQUnD1Q//OEP42ulwyczhIqzX1JwPzaQ81KnhHZgLcowKd1uXBF7+fWvf21WWGGF6PY87bVcKDPjbrrppmbllVc2/fr1M927d3d/Ptov0rGuS33Vbu2H/8A2depUM27cuA550dkRVe1F257dOv/ll182+++/fyYit+5MWjpcU17RuqBIfRrKXqqU30zwNTjp5n2e+rGZA6t2/oqjiP3g4tlnnzWHHnqoOeecc8yKK64Y0ZPzsnSNhMmTJ0fLAl933XWmV69eUVyeAbjowhL/QtSnIdtzN72iruSVrPKx5JJLGukL2ZDV37DXhN7WxYE1ZH5YhlXbDyvHbrXlWbmydduCvC8U3PtlXzt9da2fNcqbdn0q/LX7GyIzZKiSv9r8XPt/7bXXzD777JOpuvtxUJIDa8j6RcP+0pRzHXxk8gH54KGzQtE+TJl0lh0vdu1FY3w8pL1YLlXqZ219bZpCb+uSv0l6Vqn/kuSVjXPrk2YrDuSZQEHS0Q72kuSwIXkiH9O5q8+JPnmeN4q0v81WENJu3+r8vCV8mwXXnsr2T+U33PatLg6sbvmTNJZ5ftO2F0lHEXsuMx4rv1ElFElfs/Lm2pdGex5Snkb/tAr3pHtD9F8089dNs0Z5c+Vp7Wvqq21/aTqW7V+JPDeNGuPZaWnUjC+rr3b9rC1PGGn3h7K4u3mftz0PVT5mz54dTXCTlV6335D0vJ91b9lzRfTN0/66zOtc3jTq5xDlQzs/rF2Eer9l5ZfZhmjPi+ZJs3RryisqK8/7S217KSKvWX+3Gdsy52vlwOo64fiDGmWUS7vH7xCIIcnLt7KhrLyLLrrI9O/fP/7ZLbbYIt5P2hGHKXGcsuGuu+6KZm+zx3XfytLUgwYNipIpD0QyA+s888zTMFOX+yWXzMbQs2fPeEYvGcCX2X1saAd7GT9+fOSoa9O8++67m7feesseFtpq2ovbYPtlTWZWlJmMbcg7gNiq/LDp0thWGdDVzA8NXfLKKOvAGiJ/NcuH6K/R4LovsURmkgOrxLu2IwNQMqv0NttsI6ei4DuwhrSXI4880qy//vr2p83YsWPNtGnTOiwHO2HChMg5J77Q2fG/QhMHH5mFLi34M61JnRLagTWkvciSkAcddFCausb9ACOPg1aSoLXXXtvITL3SzrlBltGUFxjNgmtzM2bMMKNGjWp2S3xe0/7arf2o4wNbnDEZO0Xtxa3/NOzZHYDI41zgXv/6669Htu6q556vKk/Tnt00yn4oe6lSfv00Sh2y3HLL+dHRTNnygVirg9tu5qkfmzmwaufvySefHDm8CRc7w5FNs32BZPPH2q5rr1WfE7PyI0R9Gro9t6xErzlz5pg99tjDjBw50my88caxqjKD7e233x4fd8ZOXRxYQ+dHEtui7UeSDDdOU55btvK+UHDTkrRfNX2uTRftXyWlRzPOTVuZ8qZdn4pu2v0NTV5JslyGRfNXm59teySdeWYeddOe9EIrdP3i/n4Z+0vKD4lzX9Tl6ROmydGId/MkTx+mzG+WHS/WHn8JbS9JbIrUz9r6JqUnRFxd8jdJN7cMF63/kuSVjQvR320He/EdNtx+j4y7yzsSeSdig3x8LnqlBbf9LbvilJWt3b7V+XnL6py11eqfuu1bXRxYRW+3LijTnmvbi6TJtWeN8SuRqRnc9FUtb9r1lbY8ty+k0T/VzAeRFaL/opm/vr5Vy5svT+NYU19t+0vTr2z/SuS5dXqeZw33ejsmmJauUPFl9dWun7XlCS/t/lBWHrh56fa7su7RLB9uPyBpcg8/HW56k573/es1jl19NdpfV4e6l7eq9XOI8qGdH9ZGQr3fsvLLbEO059r+Apry2sFeXPur2t8tYxPN7qmVA6vvENPMobOZcmnn/Renf/3rX825556bdnnT+LLy3Ad8+ZETTzzR3Hvvvam/J7NfbbnllvH5kC9U4x9R3HE97GVw2A7U+F/3uQ9O7s/LjH/ubIDtYC9HH320WW+99WI1JM3iiFsmaNpLiAHEVuVHGXZp97idlqIzGmvmR1r6QsSXdWANkb+a5UNYaTS4fv2T5sDqc5RlIewMnZIW34E1pL0MGDCgoQ0TB6YxY8Y0DPLIEslbbbWVJC0xuOzsgGbihV9HujMCSZTUKaEdWLXtxdXhzTffNHvuuWeqyu4ATdWXm/4MvnkfqOtSX7Vb+1HHB7ZUQ0s4kddetO3ZHYD4/PPPzdZbb52Quv+L8r8QfOKJJxo+uJKrNOWFrE9D2UuV8uuDl48RlllmGT/aNKvHOtygFOG2m3nqR/+5yf8qXTt/t9tuu6iNsuqedNJJ5phjjokO//GPfxhxdLR9HEm/fFE6YsQIe3n0MYjMKBoihKhPQ7fn4py6yiqrxDgOOOAAc/bZZ0cfHUqkOxN8fFEn7NTFgTV0fmShzdt+ZMlwz2nIc9uCvP0fNw1Z+2XTp1k/Z6WvzLmq5U27PhUdtPsbZbgUuadK/mrzc2dgaPZs5n7YLfomvdAKXb9Utb+0fBoyZIjp0aNHdFpecj344INplwaPL9qHKZMgv9+Td/zZ9k3sb1YdHw9tLzadSds89bO2vknpCBFXl/xN0q1K/Zckr2xciP5uO9iL77BhV3qwHOXjZvcjeGkX5KPqtOcOtwxXfcGo3b7V+XnL8s7aavVPXceVOjmwVm3Pte1F8qLu/UnN8qZdX2nL0+6fZpW1MufcvNB6X+HKrFqf+jpVLW++PI1jTX217S9Nv7L9K5Hn1uka49lpadSML6uvdv2sLU8YafeHsri7eZ93vEmzfLgOepLOrMnMfKflpOf9LF3LntNuf13mdS9vVevnEOVDOz+sXYR6v2Xll9m6ZU2rPdf2F9CU1w724uaJdn+ojI3499TKgVUcOL/zne/EaQw5w6hbsVpnn/iHS+yUkXfggQeaH/3oR/GvNVsyxl2SRW7yHTpjQTXdEV1FZz/IDD3SeNiQ9mJ+r732MvKS24Z2sJftt9++Yar4PF/eWP38raa9hBhAbGV++GzKHruDS2+88YbZe++9c4vSzI/cP6pwoe94KfWKdK6bhRD5q1k+JP0aDa77Ektkpjmw9u3bN3J4sY74cq0bfAfW0PbiPiDJw8Ihhxxi5IshG5rNuOG+3PDTbmXYrZ9vEi91SmgHVv93q9SnkmaZeX3hhReWXfPJJ58YGXRPCv6s1HkctJLk2LhFF13UXH755fbQNMsbe2Fd6qt2az/q+MBm8zTPNq+9aNuz26+VdGbN4CzOgOuuu26sztSpU824cePiY9nRlBeyPg1lL1XKbwPIrw7S+sldxYFVO3+lvZYZjWyQGRVkFQgJxx9/vLn//vvNsGHDzNChQ6M4+bCuX79+0X6zwbfoogr/QtSnodvzwYMHR9ys2tJnWHLJJe2hCfn8Hv9Ijp26OLCGzo8sFHnbjywZ7jkNeW5bkPeFgpuGrP2y6dOsn7PSV+Zc1fKmXZ+KDtr9jTJcitxTJX+1+fnttywjLXVEUjjqqKOMOHrakPRCK3T9UtX+bNrrvHWf/as+42Xp6dZ9eceftcdfQttLlv556mdtfbPSo32uDvmbpFOV+i9JXtm4EP3ddrCXZg4bwtOfmSdr5iqN8U6bh9rtW52ft6zOWVu3DFfpn7plrk4OrFXbc217kbyoe39Ss7xp11fa8rT7p1llrcy5EP0Xzfz1dapa3nx5Gsea+mrbX5Z+bt2ct/8s8tz75LjqeLbIaEVw051XX+36WVuecNPuD2Xlhcswb3uuWT7EX2f11VePkygrdsokCUnBvzbpeT/pvqpx2u2vy1zSVufyVrV+DlE+tPPD2keo91tWfpltiPZc219AU1472Itm/VfGJprdUysHVilUF1xwQTwzpwwg7rffftFMds0Uka9W5UExb3AfKjVeTpaRJy9PL7nkkjjJoq8s1/7yyy/HcXZnww03bJjNKsvZxt5jt+KUY1/K2jhxdhIHg1aG5Zdf3px33nkdftJ3xBXHqx/84AcN1yXNUNEu9uLO3CdK+V9dNyj69YFUbueff37DKU17CTGA2Mr8aABT4cDtICTZWJZozfzI+h3tc2UdWEPlr1b5EE4aDa77EktkpjmwyrlTTz3VrLHGGrLbIfhOoKHtRWZA23zzzeN0uA44Eilf/Nx9993xeX9HlrBfYIEFouhmbaKfZ3KT1CmhHVjld/zfLlufiix/SQAZhEmaBf2cc84xK664otwShbSXmzJTkiy39Pbbb9tLE7ebbbaZOfTQQ+NzeXSQi+tSX7Vb+yF1lzgI2JDkXGnPtXKrbS/a9uwPQPizZLqs3D6wxO+7774d+u6a8kLWp6HspUr5dVnLvv+CwZ7vKg6sIfLXbzuEmdvW+TPdWaazZs2KnFvtsfY2RH3aivb8uuuuM7169UrEISuGJD3LJl4cMLIuDqwh8kO7/dCWl5WtbluQ94VC6PRp1s9Zupc9V6W8hahPtfsbZbnkva9K/mrz858BivStkl5ohahffK5V7M+XZY+lH2NnYJXnpsMOO8yeavnWffZPe8bTSJTbV3b7H1mytcdfQtiLZv2srW8WW+1zdcjfJJ2q1H9J8srGhejvtoO95HHYWGSRRaIP7bp37x7jnTZtmhk7dmx8bHc0xjutLO32TeTW9XnL6py1LdM/TZLn1gV1cmCVtFZpz0PYS937k5rlTbu+0pan3T9NKhtV4kL0XzTzN0m3KuUtSV7VOE19te0vSze3Ts3bfxZ5bp0ux0WeuZLGs0VGK0IZfbXrZ215wk27P5SVF27e5x1v0iwf/iqZYrfDhw83L7zwQkOyxclV3iu7EyMlPe833KR0oN3+uswliXUvb1Xq5xDlQzs/rJlIXV2396Eh2nPR138GyfOuPcn/yrLTktcO9qJZ/1l+mttaObCKYocffrjZaKONYh3FqUwqFRl48YMUQhkIEc95ediXrxnkq4Y84YwzzjCDBg2KL33rrbci58ok55X4ooydsvJOOeUUs+aaa8aSP/vss8hRVZYzt2HTTTeNnF3cBk2YXHHFFfaSzK3vsCYXF7k/U3jBk36DJrf7S2H5X0LINe+9957ZeeedZbchtIO9SP5JR8UNTz/9dOJAveguy2vJS2FxSPOXDtKylxADiKJfq/LDZVllX+oMWfrYBnH6E4dqt/zZc0lbrfxIkh0qzq8P8s7AKukJkb+a5UOjwXVfYonOWQ6s8sJGOplJwXdglWtC2stiiy1mLrvssqSk5FrO99e//rVZYYUV4vvlwUqWL3PDaqutZsTJ075wdM9JndIKB1ZNe/E/lvjoo4/MqFGjGh4qk5yU015uWkdXYSdLMMksdH741re+ZU444YSoz2LPycsJeUnRLNSlvmq39qOOD2yS19r2om3PSf21pHrht7/9rfnmN78Zm6+0n+7y6/aEtrxQ9Wkoe6lafi1H2XZ1B1bRUTt/bXkT2TY8//zz5uCDD7aH5ne/+53p2bNnfCw7f/7znzt8UNZwQcWDEPVpK9pz/6MZi6Hoagb2vqpb6Z+4fRiRt9NOOxlxCrBB2tkHHnjAHkZbec6SmS1ChhD5Ye1Zq7+hLS+Lp9sW5H2hEDp9mvVzlu5lz1Utb9r1qXZ/oyyXvPdVzV9tfu6gvejw7LPPNnzYJqtDSDu/0EILNaiY9EIrRP3S8KNfHVS1P1+eHLv1wKeffmq23XbbpMtaEuc++6c942kkpOx4seb4Swh70a6fNfXVyLe8MuqQv0lprVr/JcksExeivyvpqLu95HHYED022WSTDs/PskqdrFbnBo3xTleedvtm6wP3N+rwvOWmJ23fbZfy9k+TZLnOR3VzYK3anmvbS937k9rlTbu+0pan2T9NKhtV4kL0X7Tz19evannz5VU91tZX2/7S9Cvbv3LrdCu7yni2lRF6W1Zf7fpZW552fygrH9y8z9uea5cP3yFRnFhlxv1bbrklSrpMNCcf/nfr1q1BlaTn/YYLlA6021+XuU1inctb1fpZu3xo54fNg1Dvt6z8MtsQ7bmkQ9NfQFte3e1Fu/4rYxdZ99TOgVUS63s4S5xU9O+//360FWfVBRdcsMEBRK6RwZm8Dqzi/fyb3/ym4SsHkeGGIgOYZeX5Xtj292VpZJmNQJZa8p2Fisy+KvJ8hzWJ6ywHVv/lsMxWt8suu0iSGoI7nbScSGr07A3tYC9pzgaSzx9//HFkh717927ouEhj6juwatlLqAFEyZNW5IfN+6pb+dpJHFaTgtQ5NsgS32PGjLGH8VYrP2KBLdjx64MiDqySvBD5q1U+NBpc9yWW6JvlwCrn/ZcCEichyYE1tL1ceumlZvHFF/+/BDj/77vvvshp0onqsJv04YCUAXHqFocecapPm21NhEmd0goHVvktLXsRWX5bI+2+fDAhW3HKcz8ckeslpPUN/E64fIAjsubMmWNkX5yMpZ53Q9ZXie51sl+X+qrd2o86PrBJfoawF017ThqAkHSLLUvf7Rvf+EbUF/fLiDivJn0Eoi0vVH0ayl6qll9hb0NaHdhVZmAVPbXzd++99+7gIHPllVcaeVFjg78UmsQfe+yx5qGHHrKXqG9D1KetaM+lLZOPS/3yLx9XyjNmq8P1119v5p9//sI/m/ZBYWFBGTeEyA/t9kNbntSj48aNa3iuFURiL+64hrQn4rzmhyeffNIcc8wxcbR2+mLBX+9o1s++bI3jquVNuz4VnTT7GxqMsmRUzV9tfv6SZJJ2+8wgL7F8x1WrW9ILrRD1i/09u61qf1aOu3X7hHOLA6vYUdnxZ63xlxD2EqJ+1tLXtbnQ+3XI3yQdq9Z/STLLxIXo79p01Nle8jpsiC7ykfPaa69t1Yrefe2zzz7RLFY2UmO808qSrXb7VtfnLauzdv/UynW3dXZgrdqea9uLcKtzf1K7vIm+2vWVpjzN/qnoqhlC9F9C5K+rc9Xy5srS2A+hr6b9pelYtn/lPmu4ssuOZ7syQu6X1Ve7ftaWp90f0m7PtcvHqquuak4//fQO42HNbCfpeb/ZPWXPa7a/7VbeqtbP2uVD8lAzP6xNhHq/ZeWX2YZoz2060t6VFfW/0pZXd3vRrv8sP61tLR1YZbYUeYkoS84XCUUcWEWudM733HPPDi/f3N/0Zwd1z/n7ZeWtv/760ZfD7pIxvmx7LE68Rx11lHnuuedsVNOt77AmN3SWA+vFF19sllxyyTjNjz76aKRPHPH1jrxUdmfNkaWvZQnspNAu9iIvwb///e8nqZAYl+TAKhdq2EvIAcRW5UcitBKRxx13nFlnnXUy78xyoNbIj8wfVz7p1wdFHVhD5a9G+dBocIs6sA4cODB6Se9nU5IDq1wT0l7kY4CkDwLky1hxBmgWpG0ZMmRIs8siZwNx6pbZRG2QOqVVDqzymxr2InKOPPLIKE9kPy3IzEjitGpna87rwJomz8bLC9uf/exnRmaAzxvqUF+1W/tRxwc2yW//hW8zG8hjL5r27A5AiCO7OGBn9VOlXMhXxjJjZVLQlie/EaI+DWkvVcuv5Zr2UN6VHFi18zeprd51113NO++8Y7GaDTfcMFoFwUak1fX2vMY2VH3aivb8vPPOa3hWl5cBW221lQaWwjL8DyTzCmiFA6ukRTs/tNsPbXlJg5J580SumzVrlhk2bFh8i3b6YsHOjlb97IhU3a1a3rTbS83+hiqoFGFV81ebX9qsH27y5QWWtEPy4b6EtBda2vWLmwa7X9X+rBy7dfuEc4sDq+hedrxYc/xF215C1M+a+lqba8W2DvmbpGfV+i9JZtG4UP1dSUed7aWIw4boIqv42Dpfjv2VDTTGO0WuGzTbt7o+b1l9tfunVq67rbMDq6SzanuuaS+Snjr3J0OUN+36SlueZv9U8lczaPdfQuSvr2/V8ubLq3IcQl9t+0vTr0z/yn3W0BjPTktbiPgy+ko6tOtnTXna/SHt9jxE+ZA+kUyaNd9886WaifgbiDOllCUJac/7qQIqnNBsf9uxvFWtnzXLh2SjZn5Yswj5fsv+RpmtdnvupkHLX8DK1JJXZ3sJUf9ZfhrbljqwLr300uaCCy6I0/3KK6+Y/fbbLz72d3784x+bPfbYo2GmDv8aeVkmzpxTp06NPNX9882OBwwYEC3ZJU6VSbPLFXFgld8qK09mWpWlil3nTjftMnj9+OOPJ84C6V6XtH/00Ueb9dZbr+FUZzmw+rMbTZw4MfoCsSFxXx34TnaTJk0y4miXFdrBXuSLanEmS5tZQ/ST2frEie7GG29MVbeqvbgDiP6MvvKV0Jlnnhn/9s9//nMjS9/Y4Dr4Zc201Ir8sGmqupVpxrfeemvTv39/M++883Zwan/qqafMyJEjU3+man6kCg5w4ogjjoicNKzoog6s9r4Q+Vu1fJRtcGWmUcl/Ce6AoxwfdNBB0QzQsp8WkpyJ0hxYRUZIe/G/2Cr68CMPyrvttluqo5osASazYsnHHz/4wQ9iJEXbyvjGCjtV7cX+tCzbseWWW9rDhq30L2QpB3emXdde3Itl6TcpF8suu2wqP7le+i3/+7//mzr7syszab+z66t2az+kvF1++eUxSukvysxwnR1C2YuWPbsDEPKxkbxQk76ZO3ueZSgzycusMVlLcWvLs7+tXZ+Gtpeq5Vf09gdaLIt2cWAt8jyomb9u/1VWuZC2zg9uP6IVPEPWp6Hbc+G30047xQjlw5ZRo0bFx63cqbsDq7DQzA/t9kNbnvYLBe30pdmmRv2cJrtqvEZ506xPRR+t/kZVNnnvr5q/2vxk6cB999038ZlBPm6T5w9ZjrlPnz6RilnPdJr1SxJPDftz5bp9ws52YHWf/Vvx4UrZ8WLhpzX+omkvIetnLX1d2wu9X4f8TdKxav2XJLNIXMj+rk1HHe1l3XXXbZhRfvLkydFqQjbN/lacHc4+++yG8Wh3zN19Tpk5c6YZPny4LyI+zjN+ZS/WbN/q+Lxl9dTun1q57tZtU+RD+EMPPdQ93en7Gu25pr0IkLr2J0OVN9FZu77SlKfZPxVdNYNm/yVk/lqdNcqblVV1G1JfTftL07No/8p91tAYz05LV6j4ovradGjXz1rytPtD2u15qPIhE4GIP8EKK6wQf6BkV7u89957jfjFiI+O9UnyP1yy+Rpqq9X+tmN506iftcqHzV+t/LDyJH11fB8q6dNsz62+dqvlL6Atr672Eqr+s/yqbis5sFb98SL3S0O32mqrRUsZy8Diyy+/HDkVSSekqwUpZGuttVakq8wMJEtXyktBQn4C7WAvMnvhGmusYZZbbjkjL9LlqxtxUn7xxRfzK/rVle1gL+2QH4WgZ1zcDvmRkfxSp0Lkr1b5KKVQC2+qq71Inoozu3yt9d577xn5YvW2224rNFtoCzFGs8FWqU8XXnhhIw/A0s/47LPPIp3//ve/F66Prc7y8mHllVeOnOJ79uwZvYQQhuIQe//999vLOn1bV/tzwYSoX1z5ddjXthcNe/YHIOQLSQmy9IW8rJYPcWRwRWbIf/3115ti1JaX9IPtYM9J6SYuHwHyNx8n/6pQ7bnMuCwfbNggDu4PPPCAPWSbQkA7P7TbD215KRhKR9c9faUVa3KjdnnTqk81+htNVK/laS1+opy8oJR6QfpV8oG/fIxYNmjXLzYd2vZn5bItT0Dyuur4uLa9hKyfNfQtT7v1d85t+moThl95oprtW/lUcGcoAtrtuZa9zK39Scln7fpKS55m/1TbnkXHdnhfoV3etDmGkKdlf1XT1orx56ppDH2/Vv1s06ktz8qdm7Yyfur7fbjOY9OnTzejR49uKRKN9rcdy5t2/axVPjTyo6UGVPHHQrfn2v4lWvKwl/yG0zYOrPlV4koIQAACEIAABCAAAQhAoCyBtAGIusgrmw7ugwAEqhPo27dvNFuAlfThhx+aHXbYwR6yhQAEFAlQ3hRhIqowAeyvMDJugAAEIAABCNSOAO157bKEBHVhApS3zs1c7fHsztWGX++qBPwl3mXiIFmBpd1Cu5U36ud2szDSOzcTwIF1bs59dIcABCAAAQhAAAIQgIBHQHsAQluel1wOIQCBFhI4/fTTo5nf7E82WxLVXscWAhAoToDyVpwZd+gRwP70WCIJAhCAAAQg0FkEaM87izy/OzcSoLx1bq4z/ty5/Pn1fAROOeUUs+aaa8YXjxs3zkydOjU+bpedditv1M/tYlmkEwLG4MCKFUAAAhCAAAQgAAEIQAACMQHtAQhteXFC2YEABFpGQJa82m233cyQIUPi3/zyyy/NlltuGR+zAwEI6BCgvOlwREo5AthfOW7cBQEIQAACEKgTAdrzOuUGaenqBChv9chhxp/rkQ9zcypuuOEGM3PmTHPNNdeYhx56qAOKYcOGmaFDh8bxb7/9djTWGke00U67lDfq5zYyKpIKga8J4MCKKUAAAhCAAAQgAAEIQAACMQHtAQhteXFC2YEABIISWGKJJcz5559v5plnHtOjR48Ov3XHHXeYM888s0M8ERCAQHEClLfizLhDjwD2p8cSSRCAAAQgAIHOIkB73lnk+d25kQDlrX65zvhz/fJkbkuRa4OffPKJmTNnjpk9e7ZZaKGFTL9+/UzPnj0bkIwdO9ZMmzatIa5dDlxdH330UXPUUUfVJunUz7XJChICgVIEcGAthY2bIAABCEAAAhCAAAQg0DUJaA9AaMvrmtTRCgL1I7D00kubCy64IDFhdRucTEwkkRBoIwKUtzbKrC6YVOyvC2YqKkEAAhCAwFxHgPZ8rstyFO5EApS3ToSf8tOMP6eAIbplBFwbzPpRWdHqlltuMRMmTMi6rNbnXF3rNkZM/Vxr0yFxEGhKAAfWpoi4AAIQgAAEIAABCEAAAnMPAXcA4oEHHjC/+MUvKimvLa9SYrgZAhDITSBpwO+DDz4w999/vznjjDNyy+FCCECgOQHKW3NGXBGOAPYXji2SIQABCEAAAq0iQHveKtL8DgSMobzVzwoYf65fnsxtKRo/frzp37+/6d69e6rqMivriSeeaJ555pnUa9rhRJ3LG/VzO1gQaYRAOgEcWNPZcAYCEIAABCAAAQhAAAJzHYHBgwebbt26RXprLGOjLW+uyxAUhkAnElhnnXWiX//iiy8ix9VOTAo/DYEuT4Dy1uWzuNYKYn+1zh4SBwEIQAACEMhFgPY8FyYugoAKAcqbCkY1IYw/q6FEUEUCAwYMMAMHDjR9+/Y1ffr0MbNnzzbTp083jzzySEXJ9bm97uWN+rk+tkJKIFCUAA6sRYlxPQQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEAlAjiwVsLHzRCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAJFCeDAWpQY10MAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhUImnzU7wAAEAASURBVIADayV83AwBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBQlAAOrEWJcT0EIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAJQI4sFbCx80QgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACRQngwFqUmPL155xzjpl//vkjqVOmTDHXXXed8i/US9ygQYPMiBEj4kRdeOGF5oEHHoiPNXfOOuusWNz1119v7rnnnviYHQhAoL0IHH300Wa55ZaLEv3kk0+as88+u70UILUQgEAmgQEDBpiTTjop85oJEyaYO++8M/Mae1JbnpWbtKW/kUSFuK5CgPa3q+QkekAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCECgngRwYO3kfPnjH/9ounXrFqVi5syZZvjw4Z2corA/v8EGG5jRo0fHPzJ58mQjDikhws033xyLFefVZo4x8cXsQAACtSNwxRVXmD59+kTp+uCDD8yOO+5YuzSSIAhAoDyB1Vdf3Zx22mmZAq6++mozadKkzGvsSW15Vm7Slv5GEhXiugoB2t+ukpPoAQEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIF6ElB3YB0/fryZb775Im2nTZtmLrroonpqXpNU4cCKA2tNTJFkQKDWBHCgqXX2kDgIVCag7XCqLS9LQRxYs+hwrt0J0P62ew6SfghAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQjUm4C6A+uf/vQnM88880Raz5gxw4waNareBDo5dTiw4sDaySbIz0OgLQjgQNMW2UQiIVCJQN++fRvuX2ONNczIkSPjuCIzsMpN2vLihHg7OLB6QDjsUgRof7tUdqIMBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABGpHAAfWTs4SHFjDObBefvnlpnv37lEOT5kyxUycOLGTc5ufhwAEyhLAgaYsOe6DQPsS+O53v2vGjh0bK1DUgTW+8esdbXlWPv0NS4JtVyRA+9sVcxWdIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIFAfAjiwdnJe4MAazoG1k7OWn4cABBQJ4ECjCBNREGgTAtoOp9ry2gQjyYRAJQK0v5XwcTMEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEINCEAA6sTQCFPo0DKw6soW0M+RDoCgRwoOkKuYgOEChGQNvhVFteMW24GgLtSYD2tz3zjVRDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoF0IVHJg3W677Tro+dOf/jSOe+ONN8zkyZPjY3fnn//8p7n//vvdqLbdX2KJJczaa69tVlllFbP88subxRZbzHz00UdG9J85c6a59NJLU3VLc2Ddc889zeDBg03v3r3NF198YWbNmmUefvhhc8MNN6TKSjoxaNAgIw4bq666qllmmWUiWS+99JKZPn26ueuuu8zLL7+cdFtmXBV9N9hgAzN69OhYvtjHhAkT4mN/Z5tttjHdunWLo9P0X2uttcyiiy4aX+fvPP/88+aFF17wozscb7nllma++eaL4u+77z7z4osvRvsa+fGtb33LDBkyxKy++upRvkqeShn429/+Zt58802z7LLLmhVXXDH6vffee69TysePfvSjKH0DBgwwvXr1Mm+//bZ59tlnzaOPPhqlswMwLyIkP++nKh327dvXDB061Iieffr0ifLj3//+t3nllVfMM888Yx577LFc/NtFXwurav5aObJ16/8pU6aYd955Jzot9ZaU28UXXzyyIbHl1157zVx33XXmiSeecEU07EsZXm+99cxqq60WlWWpp6QM3nHHHVH5KONAo1n/aevboHxND+psL6GQVWnfbJraqX7R0NfqrVnerExth1MNefQ3bO6U27ZT+SinYfJdda5PaX+T84xYCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCECgNQQqObDefPPNpVMpzoQHHXRQ6fvrcuOhhx5qNttss8zkfPnll+bJJ580o0aN6nCd78B62mmnmfHjx5t55523w7USIQ52++23X+I5P1LkiFNkWpB0XXvtteaqq65Ku6RDfFV9iziw/uY3vzH9+vWL0/Dxxx+bAw880Lz++utxnN35/e9/b7p3724PO2zvuecec9JJJ3WI9yNC5cfJJ59sxEEgKUg+/OIXvzDDhw+PnCnlmg8++MDsuOOOSZcHiZN8GTlyZCbD999/3xx11FHmueeeS01DKH6pP1jwhDghyt83v/nNpneKE7roKw6taaHu+tp0a+WvlbfUUkuZCy+80B6aqVOnmnHjxkXO+uK4mhYefPBBc9xxx3U4/ctf/tKIg3dSkPIh5w844IBC5UOz/tPWN0nPOsXV3V5CsaravrVb/VJVXzcfNMubK1fD4VRbHv0Nl2j+/XYrH/k1y76y7vUp7W92/nEWAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhAITwAH1pKMV1ppJXPCCSdEMzfmFSEOWHZGT3uP6wAns6HKLKI9e/a0pxO3r776qtl3330Tz0mkvCw/7LDDUp1g/RtlllhxnswKWvpK2vLMwHrZZZdFM9naNH344YeRA9vs2bNtVMM2hEOJVn5cfPHFZskll2xIr3/w+eefR7Pj2tlfW+nAOmLECLPJJpv4SUo8FmdC0UfsNimEsOek3ykbd9ZZZ0UzJee9X/SdOHGiufHGGxNvqbu+kmjN/LUQfIdOmc35O9/5jllggQXsJYlbmZV67733bjh3ySWXGJkFMitIPsgMudZJPat8hKj/NPXN0rMO5+puLyEYabVv7VK/aOkreRGivLl5PLc4sM4N/Y12KR+u/VXdr3t9SvtbNYe5HwIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQ0CBQyYFVXszK8ttuECcmG8Tp8KmnnrKHDVtZoluWlG7XcP3115v555+/Iflz5swx//znP6Ol6mWJ1OWWW86I41O3bt2i65o5sLrCxNFLlm6Xe2XJ+QUXXNA9HTmlJc0MKY5gMnPpPPPME1//xRdfRPkgM2eKc+zAgQM7zMx6ww03RI568U3ejpa+eRxYr7zySrPIIovEKXj33XfNsGHD4iXK4xPOjjgTL7TQQnGMcBMnHRvKzMBq75Vt2fyQWU033nhjV1Qkyy6lvsYaazQ46toLsxz07DUaW985SGTKTLcyY/Cbb74Z2a9wtI6Dcl7saauttpLdDsF16HRPluXnytDYdx1oRA9Z2l5m9J01a5YRJ+L+/ftHjpg9evRo+LlDDjkkcebZuuurnb8Wiu/QKSxtPSfXfPbZZ0bqQ2kDpI2QGW+lTvIdWI844giz4YYbWrHRVmaZnjFjhpE8WHPNNRM/EkgrH6HqPy19GxSt4UHd7SUUMq32rV3qFy19Q5U3N599m7z66qvNpEmT3EsK7WvIo79RCHl8cbuUjzjBFXd8WxNxVfpXNjla7RHtryXKFgIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQ6GwClRxYkxL/pz/9KXaeFCekUaNGJV3W1nHiiLr55pvHOsjsgDJjqDiB+qF3797R8vArr7xyNINo1gyscm+arPPOO88sv/zysfhHH300Wt48jvh651e/+lWD46Y4jI0ZMyZy0nOvlRk3Zflg63QmDmi77757opOopr5ZDqzCasKECQ3Lu7/11ltRuty0592/+eab40vLOrBWzQ9/ZthbbrnFyFLLbpBl6ocMGeJGmTQHvYaLFA7E2blfv36xJHHo3GeffeJj2REnJbE/12FbZtw89dRTG66TA9+hsyq/Dj9QMeLEE080Uhb/8pe/ZDpsy3WuM7443Es58kPd9dXOX6u/70Bj48UJ+JxzzjG33367jYq2iy22mBFnGXHEl/rEBr98iGPd5Zdfbk9H21NOOSVyZHUj08pHiPpPfldLX1eHOu7X3V5CMNNs39qhftHUN1R5c/PZdwKsgwOrmz67T3/DkkjftkP5SE998TN1r09pf4vnKXdAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAJhCODAWpCrOGJdeumlseOn3H788ceb+++/P1OSOCjefffdHa7xHeDSHC19Byp/JkMRPHjw4P/P3p3Ay1HV+QI/4QFhlX3LSMAIGFafAyPKRx38gDoCQQIMKgRQdjBCICKyIyAQFlmEICiyyzKAOIhPB/ighscDZBmBgBA2AxEEwiKbLAOPf89UWV23+97bt6vv7Zv7rc8Haz99zvdUVbef/O6pWl2yD4mRnrbddttstcd80qRJ6Stf+Uq+Pep33HHH5euxUHV7mwVYl1lmmVqws/gK8kZtrKtcHytVBEra6Y9yUOiRRx5JU6ZMaVjriy66qG4042YBvYYnD3BjBDQjUJJNMWrmxIkTs9W6+UorrZR+9KMf5duajcJa5fWcf9gQLVxzzTVpwQUXrH16M5tubm8n+jfrivLzKLaH0e67756ef/757LBe5+X7I0YljpBro+mnP/1pXbC90f3RiedfVpcq2puV1a3zbr9eOuFW9fdbK3UciudLle3t5P1WdBxJAdZ5+fdGsU/7szwU90d/6tXfY7r9eer7t7896TgCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAZDQIC1ReVddtklbb311vlZc+bMSXvuuWe+3upCOQC38847117d3qicGHls8cUXr+2KV3Nvt912dYcVX88aO6ZNm5ZmzJhRd0x55aqrrkoLLbRQbXOjwGjV7W0UYI2gwllnnZXXIyoTrxHfY489ytVtab2KAGs7/RFB5+WXXz6vc29B569+9atphx12yI9tFNDLd1a08K1vfSttvPHGeWnNRlXNDjj33HPTmDFjstXaSJs33HBDvh4LVV7PdQUPwUp51OMtttiiRy26ub2d6N8MoFGgM+7juOb7O5XvjyOOOCLdfffdDU8vP4ca3R+deP5llamivVlZ3Trv9uulE27l66rd7/NW6jgUz5cq29vJ+63oOJICrPPy741in/ZneSjuj/7Uq7/HdPvz1Pdvf3vScQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIDIaAAGuLyuVXi8f67bff3mIpfz+8GIDra8TU4utI41XdW2211d8Len/p4osvTksttVS+7YILLkjxCvdsGjVqVLaYjyC75ZZbpiWXXLK2vVGZVbe3HGANu//9v/93Gj16dF63J554Ik2ePDlfH+hCuwHWdvvj6quvztv11ltv1QWfG7WpWN9GAb1G57Sz7eSTT07jx4/Pi4jRYWOU2GZTBKZ32mmnfPfPfvazdN555+XrsVDl9VxXcAdXxo0bl1ZZZZW04oorpuI9EuHeYmC3rwBru9dL1U3sRP9mdSwHOhs9O7Jjm82L4fkq7o9OPP+yulfR3qysbp13+/XSCbeqv98a1bGbni9VtreT91vRcaQEWNv9/uj23xvFPi0ud9P9UaxXu8vd/jz1/dtuDzufAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEqBQRYW9SM16jH69SzqVGoLdvXn3kx8NdoBNRiGaeddlpabbXVapsimDphwoTi7nTttdem+eefv25bqyvlEcCqbm85wNqoftddd10655xzGu1qaVsxENrs1bzlAqvsj2hHFoh86aWX0qRJk8ofV7de7L/BCLCWR+Dq61recMMN0+GHH57X+Y477khHH310vh4LVfrVFVzxSox4GwHwRRddtN8l77jjjunFF1+sO76b29uJ/s0aXw50Pv/88+lrX/tatrtf81bvj6J1o/ujeP/0qwINDio//7JDqmhvVla3zrv9eumEW9Xfb1kdu/X5UmV7O3m/ZY4xHykB1nZ//7X6PC32X6PnabEPql7u1vujynZ2+/O01evF92+VV4eyCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAoCwiwlkX6WC+OWvTuu++mGMG0nan4j8KPPfZY2nfffZsW11eAtRjYbFpIHzv22WefNHv27PyoqtvbnwBrfPhBBx2UZs6cmddjIAtFj4EEWNvpj2WWWSZdeOGFebWffPLJtPfee+frjRaK1oMRKCl+Xn+u5bFjx6bp06fnVW80Um6V13P+QRUuxGhvJ510Uj4ybitFf/3rX0/PPfdc3Snd3N5O9G/W+HKg86GHHkpTp07Ndvc5X2KJJdKll16aH/fUU0+lvfbaK19vtFAcYbDR/VG83xud359t5edfdk677c3K6eZ5N18vnXJrtc191aPbny9VtreT91vReaQEWOf13xvRp91+fxSvu3aXW73X+vP7KqtTu99Hvn8zSXMCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBLpFQIC1xZ4ojlr15ptvpm222abFEuoPLwbg4vXt8Rr3ZlNvAdbyP36//fbb6f77729WVNPt8drTl19+Od9fdXubBVjfeOONtPDCC+ef29frdPMDe1koBmwGEmCtsj8ef/zx9M1vfrOX2qZUDDw0Cuj1evIAdhb7ttGIvo2KLJo2GjGuquu50We3uy1CGzEq2ujRo+uKimtt7ty5KUbJff311/N948ePT4svvni+3leAtZ3rJf+QChc60b9Z9coBmhkzZqRp06Zlu/ucr7rqqunMM8/Mj+tPwPvyyy9Piy22WO2c8v3RqedfVsF225uV083zbr5eOuVWbHO73+fD4flSVXs7fb8V+3ukBFjb+f4o90c3/t4YDvdH8bprd7l4r1X1+yqrU7vfR75/M0lzAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgS6RUCAtcWeKIao+vuP0r19RJWBv2K48JVXXknxmtZ2p6rb2yjAGq8yPeecc9IVV1xR90r3Vkd1LLe16DHYAdaoS/Hz+/OK9Z/97GdpgQUWqDWjHNArt62K9Vb7drXVVksRos6mWbNmpf333z9brc2rvJ7rCq5gJQLEX/jCF/KSIrAW7YnwZaMpAplrr712vmu4BVg70b8ZRjlAc9NNN6VTTz01292vefH+eOaZZ9Juu+3W63nXXHNNWnDBBWvHNLo/iuVV9fzLKlRFe7OyunXe7ddLJ9xabXNvdRgOz5cq29vJ+63oLMD63xq9/QFTHFHsj278vTEc7o/iddfucqv3Wn9+X2V1quL7qHi9+P7NZM0JECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEBgqAQHWFuXjFeox2lU27bjjjunFF1/MVlueVxn4K75iO0ZgnThxYsv1KZ9QdXvLAdZisDRGvIzXu48aNSqvxvnnn5+iXQOZiv9AX/yc3sqqsj+KgdQY2XO77bbr7aPrAiiNAnq9njyAneeee24aM2ZMfuYWW2yRLzda+MxnPpO+/e1v57tuueWWdMIJJ+TrsVClX13BFazEK+tjFLiYIny+3377pXhtc7MpRmtdfvnl893DLcDaif7NMKoI0BRHqPvrX/+att9++6z4hvMIumfPhkb3Ryeef1lFqmhvVla3zrv9eumEW5Xfb8Ph+VJlezt5vxX7uhxgjWDgJZdcUjykpeWqy8s+3O+NTKLxfDjcH41rPrCt3f48Henfv/H/41ZZZZUenRsj8d933309tttAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBnBToaYG00QmOrzYlQ0+qrr153Wrzyu5XXVded3ObKoYcemj75yU/mpUTILUYGHOhUZeCvHLjrK5DYnzpX3d5ygPX6669PZ599dl6VXXfdtS54++6776bJkyen2bNn58f0d2GoAyXFwEbUubewczlU0yig19929/e44447Lq233nr54cccc0y6/fbb8/Xywp577pkmTJiQb24UJKryes4/qKKFYt1eeOGFtNNOO/VacnHEzzhwuAVYO9G/GVgVgc7iCHVxn2+55ZZZ8T3m5VdkN7o/OvH8yypSRXuzsorzbvp+6/brpehW1XKV32/D4flSZXs7eb8V+zd+/xVHd/6P//iPdMYZZxQPaWm56vKyD/d7I5NoPB8O90fjmg9sa7c/T0f692/8/46VV165R+fOnTs37bzzzj2220CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQGcFKg+wFkedfPbZZ9Muu+zSVgvKIcAoLEZPLAbp2vqAFk/edttt09e+9rX8rP6MrJkf3GCh+I/6jzzySJoyZUqDo/57U1+vkI0A4sc+9rH8/EYjZOY7+7lQdXv7CrBGtcojV7388stphx126GeN/37YUAdKyq+gv/HGG2uvrP97Df++VD62UUDv70dXs/SNb3wjffGLX8wLe+ihh9LUqVPz9fLCZZddlhZffPF8c9R5xowZ+XosVHk91xVcwUpxBM+nn3467b777k1LLV/3ceBwC7B2on8zsCoCneUAyTnnnJOijxpNhxxySNpoo43yXY3uj048/7IPrKK9WVnFeTd9v3X79VJ0q2q5fJ+3830+HJ4vVba3k/dbuX+L3+UxOuLBBx9cPqSl9arLiw8vljkUI76Xf0N02++N4XB/tHQR9XFwtz9PR/r3b7n9WXcKsGYS5gQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQGV6DyAGu82nXJJZestaKvUfX609RuCvhk9S2+Oje2lUcRzY4rzuMfs88666ziptpylYG/CFn98Ic/zF+zHUHfPfbYI0VYr69p4sSJKcLHjaYq29ufAOtSSy2Vzj///DT//PPn1bnjjjvS0Ucfna/3Z2GoAyXjxo2rGynunXfeqQWUn3jiibrqr7322umEE07I+y12Ngro1Z1UwcoKK6yQzjvvvLykuF722muvNGfOnHxbtvCZz3wmffvb385W05tvvpm22WabfD1bqPJ6zsqsan7FFVekRRddtFZc9MVWW23VtOjyNR8HDrcAayf6NwOrItD5uc99Lu23335Zken555+v++OAfMf7C8U/jIjtje6PTj3/4vOqaG+UU5666fut26+Xsl1V6+V7faDf58Pl+VJVezt5v5X7tnj/9/XsLp/baL3q8uIz/N5oJP33bcPl/vh7jdtb6vbn6Uj//hVgbe/6djYBAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBqgUqD7AWRwmNyj7zzDMpRoaaNWvWgOreTQGfrAGbbrppj5FSH3744XTAAQdkh+TzDTbYoBb8W2SRRdI+++yTZs+ene+LhaoDf9/61rfSxhtvnH9GhIivvPLKFMHi8hQBlAjlRR0jLBp9F6N2lacq29ufAGt8/iabbJL233//uqqccsop6eabb67b1tvKUAdKom7Tp09P8frzbIrwTYww+8tf/rK2afPNN0977rlnmm+++bJDavNGAb26AypaOf7449O6666bl/b222/Xrtfi/Rr9H0HDUaNG5cfFNXXRRRfl69lC1ddzVm4V8x/84AfpQx/6UF5UBIknT56cr8fCWmutlWJkwdGjR9dtj5XhFmCNOlfdv1FmTFUFOouhpij30UcfrQu1xh9DRNCkOPJvHNfs/ujE8y8+r6r2RlnFqdu+37r9einaVbVc1ffbcHm+VNXe8O/U/Vbu25NOOimtueaa+eYXX3wxnXnmmen222/Pt7WyUHV58dl+b/TeA8Pl/ui9Fa3t7fbn6Uj+/hVgbe1adjQBAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBTgtUHmCN0SQjsNpoivBeNj344IP9eg1stwV8svo3+8fPeAXx3/72t1rYb4kllqgLJg5GgDXqVx5hLbaF/auvvlqbR1h1scUWqxvhNI5pFmCNfVW1t78B1vjM7373u2n99dePxdoUbdhtt91qIzVm2yJYduqpp9Y5x74IWxZDiBHkfeutt7LT8vkf//jHdNhhh+XrVQcwx48fn0488cQe9cs/sMlCs4Bek8MHvLk8SlhWUFzHL730UlpmmWXqHGN/s9FXY1/VflFmVVMEtY866qi64uKaipD9QgstlCJkHv81m4ZjgLXq/s1sqgp0ll9pHuXHvfrKK6/U7plycDX7/N7uj048/6pqb1b/bN5t32/dfr1kblXPq/h+G07Plyram/VBJ+63rOxsHtflj3/847o/osj2ZfMYQXzChAnZaq/zgZbn90avrL3uHE73R68NaWFntz9PR/L3b7Nn4Ny5c9POO+/cQi87lAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBKgQqD7BGpY444oj08Y9/vNf6NRr9sNEJ1157bY+gZW8BukZldGrb4YcfnjbccMN+Fz9YAdalllqqNorkqquu2u+6xYG9BVhjfxXtbSXAGp95+eWX18K2sRzTs88+m3bZZZf/Xnn/fxuFIvKd/Vh48skn0957750f2YkA5uqrr14LdS+44IL555QX4n6IwHP0XUy9BfTK57a7/qlPfao2kl4Em/uaIgR9yCGHpMcee6zhoZ3wa/hBA9wYdd9oo436PDvCzhGy/+hHP5ofOxwDrFH5Kvs3w6gy0BnPxc022ywruuE87ocIqEXwPqbe7o9OPP+qbG+xgd34/dbt10vRr8rlKr7fhtPzpYr2hn8n7rdG/RphuwiWFUcCLx+3xRZblDc1XR9IeX5vNOXs147hdH/0q0H9OKjbn6cj9ftXgLUfF69DCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECAyiQEcCrFH/eE3tVlttlcaMGZMWWGCBHqGDhx56KE2dOrXXpkbwL0bXLE/NXl9ePm4w1mOE0HiNbrORAqMOL7zwQoqg0jXXXNOjSgMN/MXIkeHb2/SlL30p7bTTTj1G0CyeE6MdRhjxpptuStddd11xV8Pldtv7iU98om7E0+uvv742umvDD3t/Y1wD3//+9+uun3h1fVwDMXVLoKSv/lh22WVr13u8wj4L4WWjf8ZrkM8///xam7IRQMtB3VpjO/g/MdLqCSeckFZaaaWGnxLhwfvvv7/PUZM7eT03rNgANkZwadKkST2C8VlRjz/+eO0ajbDU5z//+WxzahSOGg7tjQZU1b8ZRpR34YUXZqu150ejZ3V+QB8Lm2++edp9990b9km8LnzfffdNp59+elp66aVrJfUWYM0+qsrnX9XtjTp28/dbt18vWR9XPW/3+y3qM5yeL1W0N+uDKu+3rMzyfNy4cWm//farfU9l35XFYxo9o4v7y8utluf3Rlmw9fXhdH+03rrGZ3T783Qkfv+eeeaZqdEf+RmBtfE1bCsBAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBTgt0LMBaRcX33HPPHq+E7ZbRVxu1L0ZrXGedddIqq6xSe/16jKoZob/Zs2c3OnxQt0VwdK211qq9Kj3CiHPmzElRv3vvvXfA9ejm9g64UYN04tixY3tcF8Uw5MyZM9NBBx00SLWp/5gINa233nq1a+Xll19O99xzT2000vqjhv9a3BPjx49PMbpmvLL+mWeeSTfccEOKwOS8PHVz/0agLPol/iDgz3/+c79C9f3pq048//rzub0dM1y+37r5eunNt9197X6/DbfnS7vtLXp34/1WrN9IW+7G3xvD7f6o6prp5ufpSPr+rao/lUOAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQDUCXR1gbfSKx4svvjhdccUV1bReKQS6RKD8ivIIUsaIkyYCBOZNAd9v82a/ahWBbhfwe6Pbe0j9CBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMLIEujrAeu2119a9UvqNN95I//qv/zqyekhrR4TA8ccfn9Zdd928rfE69ptuuilft0CAwLwl4Ptt3upPrSEwXAT83hguPaWeBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQGBkCXRtgjdfdn3jiiXW98KMf/Sj9/Oc/r9tmhUC3C1x99dXpkUceSZdffnm65557elR37733Tptvvnm+/aWXXkqTJk3K1y0QIDBvCfh+m7f6U2sIdIuA3xvd0hPqQYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQL9FejaAOs+++yTNttss7wdr776avrKV76Sr1sgMFwEfvGLX+RVffPNN9MLL7yQnn/++bT44ounFVdcMS200EL5/lg4+uij0x133FG3zQoBAvOOgO+3eacvtYRANwn4vdFNvaEuBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINAfga4NsG633Xbp85//fHrvvfdq/1122WXp5ptv7k+bHEOgqwSKgZLeKhbX+i9/+ct09tln93aYfQQIDHMB32/DvANVn0CXCvi90aUdo1oECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0FSgawOsTWtsB4FhJjB9+vQ0ZsyYNP/88zeteYzKeswxx6RZs2Y1PcYOAgQIECBAgEAzAb83msnYToAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLdKiDA2q09o17znMC4cePS6quvnpZbbrm09NJLp+effz7NnDkz/eEPf5jn2qpBBAgQIECAwNAI+L0xNO4+lQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRaFxBgbd3MGQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAm0ICLC2gedUAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB1gUEWFs3cwYBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAbAgKsbeA5lQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoHUBAdbWzZxBgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQhoAAaxt4TiVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEGhdQIC1dTNnECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQItCEgwNoGnlMJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRaFxBgbd3MGQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAm0ICLC2gedUAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB1gUEWFs3cwYBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAbAgKsbeA5lQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoHUBAdbWzZxBgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQhoAAaxt4TiVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEGhdQIC1dTNnECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQItCEgwNoGnlMJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRaFxBgbd3MGQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAm0ICLC2gedUAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB1gUEWFs3cwYBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAbAgKsbeA5lQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoHUBAdbWzZxBgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQhoAAaxt4TiVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEGhdQIC1dTNnECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQItCEgwNoGnlMJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRaFxBgbd3MGQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAm0ICLC2gedUAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB1gUEWFs3cwYBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAbAgKsbeA5lQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoHUBAdbWzZxBgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQhoAAaxt4TiVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEGhdQIC1dTNnECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQItCEgwNoGnlMJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRaFxBgbd3MGQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAm0ICLC2gedUAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB1gUEWFs3cwYBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAbAgKsbeA5lQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoHUBAdbWzZxBgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQhoAAaxt4TiVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEGhdQIC1dTNnECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQItCEgwNoGnlMJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRaFxBgbd3MGQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAm0ICLC2gedUAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB1gUEWFs3cwYBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAbAgKsbeA5lQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoHUBAdbWzZxBgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQhoAAaxt4TiVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEGhdQIC1dTNnECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQItCEgwNoGnlMJEOiswOmnn54WXnjh2ofceOON6corr+zsB7ZY+rhx49Kxxx7b61lnn312mjFjRq/H2EmAQN8C7re+jRwxMIFDDz00rbLKKrWT//jHP6bvf//7AyvIWQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBLAm0FWPfff//8H/zjU4855pg0d+7cphUYO3ZsOuCAA/L9Eeq6+uqr83ULw0Ng++23TxtvvHFafvnl0/zzz59X+q233kqvvPJK+t3vfpfOO++8fLsFAgMV+Pd///c033zz1U5/5JFH0pQpUwZaVEfOW3vttdO0adN6Lfuyyy5Ll156aa/H2EmAQN8C7re+jRwxMIGLLrooLb300rWTX3vttfTlL395YAV1yVnbbLNN2mSTTWptWmSRRfLv0TfffLP2Oz3ae8stt3RJbVWDAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBEayQFsB1uI/+AfiPvvsk2bPnt3UM/5B/etf/3q+/+67705HHHFEvm6huwU++9nPpsmTJ6fRo0f3WdH33nsvnXzyyem3v/1tn8c6gEAzAQHWZjK2Exh5AgKsI6/PB6vFxd+zwznA+o1vfKMWXF1wwQX7pHvyySfT3nvv3edxDiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAp0UEGDtpO48VHY5fNyfpl1wwQXpqquu6s+hjiHQUKDbA6xR6eWWW66u7uuss06aOnVqvs0IrDmFBQJtC7jf2iZUQAOBeSHAusIKK7Q8+v1DDz1U933VgMYmAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQUQEB1o7yzhuFb7TRRumQQw6pa0yMsPqnP/0pzZw5s/bfyiuvnNZcc83af9kIrQKsdWRWBiAwHAKs5Wb94z/+Yzr66KPzzQKsOYUFApULuN8qJx2RBc6LAdaXXnopRUD13nvvTS+88EJaa6210he+8IVUHp311FNPTTfddNOI7HeNJkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGhFxBgHfo+6PoaRBB12WWXzev5zjvvpEMPPbQWXM03FhbiFbYRkjj//PPTz372s8IeiwRaExBgbc3L0QRGmoAA60jr8c60d14KsL722mvp3HPPbRpKvfLKK9MiiyySQ952223p2GOPzdctECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBwRQQYB1M7WH4WRtuuGE6/PDD85pHePWAAw5Ijz32WL6t0cJSSy2VXnzxxUa7bCPQbwEB1n5TOZDAiBQQYB2R3V55o+eFAGugxB8P/frXv+7V54tf/GKKPzTKpr/85S9p1113zVbNCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAoAp0fYB1++23Tx/5yEfScsstlz7wgQ+k//qv/0p//etfU7wa9c4770w///nPWwKL19xH4GX8+PEpXnv/7rvvpqeeeqo2mugtt9yS5syZ0+/yttlmm/zYG2+8Mb388su19Q022CBNnDgxLb/88rVRrl555ZX0zDPPpBj16oEHHsjPabTw2c9+ttbO4r7nnnsu3XrrrcVNg7b8k5/8pNaO7AN/+9vfppNOOilbHfA8+nPzzTdP48aNS0svvXRaYoklan375z//Oc2aNSvdd999tf7t6wMmTJiQvw7397//fZo9e3btlJ133jlFP0S50cdPPvlk+s///M909dVX91VkJfvXWGONtO666+ZlxQhn/b22itfVH/7wh/TII4/k5ZQXIoiy9tpr1xxjRLW4Lx599NHaK4Ojr/qaNt100zR69OjaYXfffXd6+umnm54S7Rk7dmxtfwSZ+wrJNC2ohR3NAqxV9W+Vz4OsWe0E6op9//DDD9fugzDfeOONa8+tGAk57pF4JfTvfve7/HrPPrtT86jDP/3TP9WKf/3111Ncz5MmTUof+9jHUlwLd911V+3eev7559NKK61Ue/6Fw3zzzZeiHZdcckntOdtX/TrRH319Zqv7V1hhhbT++uvXvpdWXXXV2ujUb7zxRnr22Wdr92o8M/uahtPzr4r2Zh6d6N927resXlXPh1P/Vtn2dr+PinUpPgur+H213nrrpU9+8pNprbXWSssss0zteRS/GX7zm9+kuXPnpoEEWKu8nqtub9GyvPzxj388HXHEEfnmeHbtsssu+boFAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwmAJdG2CNgNpWW22VFlhggT49Inh6wgkn9Hnc9OnT8wBeo4Pfe++9dMUVV9TCVo32F7f9wz/8QzrnnHPyTTfddFM69dRTUznwmR/wPwsREiwGB8r7f/GLX5Q31cIV4THYU4Qwv//979d9bIzSFaN1DXSKkEb8F2HkvqYIhR1yyCG1QGuzY8sBx2nTpqXo52bXTYT/9thjj2bFVbb9S1/6Utp9993z8mbOnJkOOuigfL3Zwg477JC++tWv5rsjYPPd7343X88WPv3pT6epU6em+eefP9vUY/7qq6/W/HobLbfsN2XKlB7lZBtOO+20tNpqq9VW416J8HCnp3L9quzfKp8HRYd2AnXF+z9Cqvfff3+KEH+z6aqrrkoXXHBBs92Vbf/6179eu2+jwAisxr25+OKL15UfQf3JkyfXXl2dhaKzA956663aiH+9BaQ71R9ZHaqY77fffulzn/tcr0XFvfHHP/4xHXjggT2OG27Pv3bbWwToVP+2c78V61fF8nDr3yraHGVU9X2U1afq31ff+9730kc/+tGs+Lp53K+xf5999qn9MU3sfO2119KXv/zluuPKK1Vez1W3t1zX8nr8Fok+y6b+/obOjjcnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJVCnRlgDVGgtp666373c4nnniiFpxqdkL8Q3289r5ZqLF8Xox42VuQL44vBw4iABCjES666KLl4urW+xrpqhhgy06M0cGGIsAaYZwIrmVTX3XPjuttfsopp9RGLuztmOK+CJecf/756ZprriluzpeLAccY4TRGVltooYXy/Y0WIkRXDJc2OqaKbddee20eMI1RYLfccss+iy2OAhcHx3VYHoF1//33T5tsskmfZcUB4fejH/0ohVOjqejX13U/1AHWqvq3E8+Dom07gbri/f/mm2/mo+MWyy8v33HHHenoo48ub650vRhgHWjBzULcne6Pgda3eF4EtyNIHiM693eKQFw2InR2znB5/lXV3mh3p/u3nfst65eq5sOlf6tqb5RT5fdRVq8qf1+dd955KUYR7m2K78kY3T/7g5DeAqyduJ6rbG9v7Yx9G220Ue0PW4rHxR+GzJgxo7jJMgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQGDSBrgywFkNcIfHyyy+n22+/PT3zzDO1cOIqq6ySVl555TRmzJgaVG8B1ggu/PjHP06jRo3KUSNM+NBDD6UYmTLCjquvvnqPkVnjVfMRnGw2lQMHUWa8Ljub3n777fTCCy+keN320ksvXRtxNOrQVwi03PYob6gCrBGejFfMZ1MVo3QVAz5hFn0aI7o++eSTtZEdo08jCFwewXHfffet9VdWl2xeDGBm22Iezo8++mitT9Zee+202GKLFXfXQjezZs2q21b1ylFHHZU22GCDvNgYKTNGzGw2ffCDH0w//OEP890vvvhi2nHHHfP1WCiHtWLb3/72t9qIj3GdxHUZ4bMsiBP7ewvPFv26PcAabcmmgfZvp54HWb1iXu6jyy67LF166aXFQ5ouN7r/4+AYOfjBBx+s3RdxPS+11FJ1ZcR9dfPNN9dtq3KlHGCNUVh//etf1wKdn/rUp3p8VIwcHEHxeKV49ocDEciNUHxxGoz+KH7eQJf/7d/+LS288MJ1p8fz/U9/+lOK7594ZXx8L8X9l30P9BVg7ebnX1XtHYz+bed+q+vQClZG0vdbcJXtY1s730dxfkxV/b769re/nT7zmc/8d6H/87/FZ+m6667bMJTeLMDaqeu5qvbWNbTBSoyqf+KJJ9b9PnjqqafSXnvt1eBomwgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwOAIdF2AddKkSekrX/lK3vr77rsvHXzwwfl6cSHCBDGSZgT9zjrrrOKufLk4amRsjOBblBehyeIUI1rG65Kz8FGEiyI8GOHZRlM5cJAdE8Gu008/vUeYbNlll00RpoggZQSbmk2NAmxDFWA94YQT0jrrrJNX9ec//3ltNM98wwAWjjnmmBQhil/96le9BoTjuAiyZlOz66AYwIxjYyS1CIpGALk4nXnmmWnVVVfNN8Xr2Q855JB8vRML48aNS2eccUZedF/h5fJrfS+//PJ0ySWX5OfHQoSxV1xxxXxbBIB32223fD0W4r6I9hYDd83Cx0W/4RBgbbd/O/U8KHZAOdTVboC1UZj+uOOOS+utt17+sX1dW/mBA1woB1gvvvjidMUVV9RKK4b2YkP8ccDUqVNr+yZOnJh23XXX2nL8TywXn72D0R/5hw9wIZ7Xm222WX52s2swDogRWo888sjaM65RgHU4PP+qbO9g9G8791veqRUtDIf+raiptWKq/j7K6lbV76viKOhRdgSzL7zwwuxjavPjjz8+RZC1ODULsHbqeq6qvcU2lJfXXHPNFG0t/nFLs3aWz7VOgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQ6KdB1AdZ4FXYEUrLpwAMPrI08mK23Mo/RL2MUzGyKkcG23XbbbLXHvByevfXWW1MExRpNjQIHMepqBGqff/75Rqf0a1s3BVjL4ZR4FX2EWAdruuaaa9KCCy5Y+7iwjTBceSoGMGPfbbfdlo499tjyYT1GdOt04C+rwEUXXVQbgTdbLwf4su0xj6BiNvJshOQmTJhQ3F0L9EZAKpuamcT+lVZaqS5s3GwU1qLfcAiwttO/nXweZH0S83YCdeX7f+bMmSmCzY2mn/70p7WRnbN9EciOYHYnpnKANZ5zMcJqTDvttFPabrvt8o8tBnYjTB2v8M6mI444It1999211cHqj+yzBzKPPzz4yU9+kv9hQ5QR3yl33nlnr8XFa7rj+6OdaSief1W2d7D6t537rZ3+affcoejfdutcPD/+wKTq76Os/Cp+X5WD2A888EDtj4iyzyjOy8/SRsHOTl7PVbS32J7y8tixY2t/TFMMr8bv4Rh5tZ3fq+XPsU6AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAYi0HUB1ggHRVAgmyLkEQGigUzlkQGnTZuWZsyY0WtR8Yr3hRZaqHZMbyHHRoGDduqaVaocYIvtQzUC65VXXpkWWWSRrGq1sMrtt9+er3d6oTxq6hZbbNHjI4sBzNi5884717x6HPj+hgjWLb744rVdr7/+el3ortHxVWz76le/mnbYYYe8qN/85jfp5JNPztezhQi8FUeEjdfFR3i7OH3rW99KG2+8cb6p2aiq2QHnnntuGjNmTLZaGxn4hhtuyNdjoeg3HAKs7fRvJ58HRdR2AnXl+//QQw9Nf/jDH4rF58t77rlnXci5r+shP3EAC+UAa/FejNGr999//7zUCP0Xw5vFNkUf3HzzzbVjB6s/8ooNYGGXXXZJW2+9dX40gbrKAAArQElEQVTmnDlzUrgPxjQUz78q2ztY/dvO/TYY/djsM4aif5vVZSDbO/F9lNWjit9X8btx+eWXz4pMxfB8vvF/FsrXfaMAayev5yraW25TcT1Gc4/R/7Mpfv9EwFd4NRMxJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGhFOi6AGsE1P71X/81N3nzzTfT9773vXzUvnxHPxbiNddLLbVUfmS8Wj5GtsymUaNGZYv5CHtbbrllWnLJJWvb33nnnbTVVlvlxxQXyoGD3o4tntfXcrwW/AMf+EDdYS+88EKK0cMGeyqOCBqfffDBB6f77ruv8mqMGzcurbLKKmnFFVdMxT6JsGYxgFkMzWWVKAYw+xphtziibFX9ldWjt3mxjnE9b7PNNj0OL4djGoVtIvg6fvz4/NwpU6akCJ02m2JUzBgdM5t+9rOf1Y2GGduLdev2AGu7/dvJ50FmHPN2AnXFsGezayX7rHi2RZuy6eGHH04HHHBAtlrpvBhgLY/mWw5fl0eCve666/L7ujiK82D1RzsQMcJkjDSZTbFedYi/m55/VbZ3sPq3nfst69dOzrupf6tsZye+j7L6VfH7qvjHSG+99VZdED37nOK8+OxtFGDt5PVcRXuLbSkub7/99in+y6b4XokQvvBqJmJOgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAkMt0HUB1njV6VlnnZUHnjKgN954Iz3++OPpnnvuSb///e97De5l51x77bWp+MrUbHsr82YjPpYDBxEG+NrXvtZK0V1/bDmwcdppp6Ubb7yxknrHyKQRDl500UX7Xd6OO+6YXnzxxbrjiwHM3kbMjZOi/quttlrt/AgyT5gwoa6sTq0cf/zxad11182LL49QGTuKIb9mo8OWR5RrFOjNP+T9hQ033DAdfvjh+aY77rgjHX300fl6LBT9uj3A2m7/dvJ5UERtJ1BXDFFFcL0YQC5+RrZcvG46+QwqBljL4e8I3cc1nU3lYHXxGouRAC+55JLaoYPVH1m9BjKPwO1KK62Un9rXPZcf2MdCtz7/qmzvYPVvO/dbH9004N3d2r8DblCDEzvxfZR9TBW/r4rPxpdeeilNmjQpK77hvPicahRg7eT1XEV7Gzbq/Y3x5oG11147333kkUemu+66K1+3QIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEhlqg6wKsARKvNt1ss816tXn77bdro5JGKPG5555reGwxDNbwgH5sjLrMnj27x5HlwMFDDz2Upk6d2uO44bzhjDPOSDF6XDZddtll6dJLL81WBzSP8k466aQ0evTols+PEF25r4uhk8ceeyztu+++TcsdqgBrhEciRJJN5ZEyv/zlL6cI52bTL3/5yzR9+vRsNZ8XR5Qrj4KZH1RYiDB4sZwnnngiTZ48uXDE8Aqwttu/nXweFFHbCdQV6xjPnXj+9DbFqLoLLLBA7ZC+RqjtrZy+9hUDrPHsnThxYn5KjAocozFmU/mZWQx+xTUcI2HHVGxrbcMA/qf8WQMootdTWr3nei3s/Z3d/vyrsr2D1b/t3G999Ver+7u9f1ttT2/Ht3qt9Of7KPu8dn9fLbHEEnW/VZ566qm01157ZcU3nBdHnG8UYO3k9dxuexs26H82Fv8QqT+/G3oryz4CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINAJgbYCrOedd15aYYUV8nr1FSaKV6dHECqb7rzzznTUUUdlq3XzzTffvDai6cILL1y3vbwSowGecMIJ6bbbbqvbVQ5LROjq/vvvrzumPysRzHr55Zd7HFoOHMyYMaMupNjjhGG44bDDDkuf+MQn8prH6KsRAh3oFKGSGLWtHF6N4N3cuXNTjJIWo49mUwTjFl988Wy1du30FmDtxhFEs8pH8DfaH1N59NfySHY77LBDw2uuGAQsl5F9TnleDN00GsG0GADuRr+q6tfp50HRvZ1AXbG/+hOKv+aaa9KCCy5Y+/jyyKjFOrW73EqANYJiERjLpuJ1mwVYB7M/snoMZF6se7x6O77DBjoNh+dfVe0dzP5t534baF82Om849G+jeg90W/Faqer7KKtLu7+vVl111XTmmWdmxaUnn3wy7b333vl6o4UYHXqxxRar7SoHWDt9Pbfb3kbtybYV/8jhr3/9a9p+++2zXeYECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKArBNoKsJaDdzG6Y4zy2GwqB1hvueWWWvi02fGxPV5Pvemmm6Y11lgjrbjiimn++efvcXizYFExDPbKK6+keK1vVVM5cHDTTTelU089tariu6KcYmgtKjRz5sx00EEHDbhu3/zmN9MXvvCF/PzotwjERvi30VR+9W3UZ7gGWMuW2avU4/Xk8drubOpt1M1iwKY/gaHVVlutLnA8a9astP/++2cfVZu3EhD9wQ9+kD70oQ/VzuvP59d90ABXWqlfXyPsdvJ5UGxeO4G6Yh3nzJmT9txzz2LRPZaLIbIIf2+33XY9jqliQ/H67WsE1v4EWKNOxbZW/Xyuos1RRqv3XG+fOxyef1W2d7D6t537rbf+anXfcOjfVtvU2/GtXiv9+T7KPq+K31fF6++ZZ55Ju+22W1Z8w3nxjwHKAdY4oVhe1c+rKtrbsFHvbywGWOOPseIPZEwECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKCbBNoKsJ599tlp5ZVXztszZcqUFKM4NpsiXLXTTjvluwcS+lx//fXTrrvummJErOIUr0m94ooriptS8ZWw5dBV3YEDWOlk4GAA1enIKREcjj7NpnZHeCyPQrrffvuleC18s6kckB7OAdZo43XXXZdGjRpVa+4LL7xQuxemTp2aPvvZz+YEp5xySrr55pvz9eLCueeem8aMGZNv2mKLLfLlRguf+cxn0re//e18V6PAeDEgGuHzCKE3my666KK09NJL13YPxwBrJ58HRbNyoC6CXpdccknxkKbLxZBUf8JGxeP/8pe/1J6NTQtvY0cnAqyD1R9tNDtNnz697rtmxx13TC+++OKAihwOz78q2ztY/dvO/Tagjmxy0nDo3yZVH9DmTnwfZRWp4vdVMdzfn5FHi9/PjQKsnbyeq2hvZleeC7CWRawTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQLcJtBVgLY54GA079NBD0x/+8IembSyGoOKgCAycc845TY/vbUcE8yKgl0233XZbOvbYY7PV2rwcgOwr8Fd3ch8rnQocRBvKo8z+/ve/r4Vx+6hSR3YXQyDxAdFf0W8DmYphySzA2Vs5xRHR4rjhHmCNcOpHPvKRvMn77LNP+v73v58WWmih2ra33norbb311vn+8sJxxx1XG5E4237MMcek22+/PVvtMY/ROydMmJBvbxSkLBrPnTs37bzzzvnx5YVigGc4Blg7+TwoWq2++up1ozH/x3/8RzrjjDOKhzRdLgZS+wqMl0c0fOCBB+oCy00/ZAA7is/u8h8DjB8/Pp188sl5qf0dgXWw+iOv2AAW4jvtk5/8ZH5m1DnumYFMw+H5V2V7B6t/27nfBtKPzc4ZDv3brO4D2d6J76OsHlX8viqOEPvuu++mLbfcMiu+xzz+ICrC29nUKMDayeu5ivZmdS/PN9poozR69Oja5vijiLvvvrt8iHUCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDCkAm0FWA888MD0z//8z3kDfvrTn6b4r9kUgbuPfexj+e7TTz893XDDDfl6KwvLLLNMuvDCC/NTHnzwwRT1KU7lz2s0AmXx+FaWOxU4KAbYsvr0FSzMjuvEPF45v8kmm+RFP/vss2mXXXbJ11tZKI5w9vTTT6fdd9+96enbbrtt+trXvla3f7gHWDfYYIN01FFH5W0Kg5VWWilf7+v6/MY3vpG++MUv5sc/9NBDKUZwbTZddtllafHFF893T5s2Lc2YMSNfj4UYGXTJJZesbXvzzTfTNttsU7c/WymHFIdjgLWTz4PMKZsX7+P77rsvHXzwwdmuXufF8+LAGOX6+uuvb3jOYYcdlj7xiU/k+wYyonV+ch8LnQiwDmZ/9NG8prvLz6HXX389xUjiA5mGw/OvyvYOZv8W75tW7reB9GOzc4ZD/zar+0C2d+L7KKtHFb+vym8I6O2Pbw455JAUQc9sahRg7eT1XEV7s7qbEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB4SbQVoA1RneMUR6zqbfXtMYIVzEKYXF00V133TXFa6+LUxwX5bz00kvFzT2WP/e5z6V4BX02RcgrAgvFKUIBP/zhD/PXtkfobo899kgRHOxrmjhxYopXrzabOhU4KAZxss8eygBr1KE4slys9zXS4xJLLFEL7MV5t956a5xSm6644oq06KKL1pb7Gl2yONrn/5w+7EdgjXZceeWVaZFFFsmaVDePe2nOnDl124orK6ywQjrvvPPyTXE9x2iXjc6J0YljlOJsahZOLb8yPEI6jUZ1jbD5hz/84ay4NBwDrJ18HuQw/7NQfG1zX9d68dzy/f/888/3CHJnxxc/I7ZFILw/z7bs/FbmnQiwDmZ/tNLW8rHlZ1Gj75ryORHuO+uss+o2D5fnX1XtHcz+Ld4LrdxvdR3U5spw6d82m5mf3onvo6zwKn5flX8jtvIsbRRg7eT1XEV7M7vyPH4XZyOwxu/qAw44oHyIdQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMKQCbQVYo+blQF68ovTwww9Pjz32WN6wj3/84ylGuCqGV5944ok0efLk/JhsIQvKxf54BWyMSlmePvrRj6bvfve7deUdffTR6Y477igfmr71rW+ljTfeON8er5KNOsfIk+UpQgQR1IqRMqOup512WrrxxhvLh9XWOxU4KAfY4sOGOsB60EEHpU9/+tN1Dk8++WT60Y9+VPc62gi0bLXVVmnzzTdP8803X7rgggvSVVddlZ/3gx/8IH3oQx/K1xtdA2uttVaKEGUWuMgPfn9huI/AGm3ZZ5990mabbVZsVm25vyPbHn/88WndddfNz4/XuUdQddasWfm2TTfdtBbuHjVqVL4trvmLLrooX88W9t133/T5z38+W01vvPFGbSTj6JtsOuGEE9I666yTrdbmwzHAGhXv1POgDuf9lZNOOimtueaa+eYXX3wxnXnmmQ3DwflB7y80uv8b3Scx0vUHPvCB/NTo/xgtuVNTJwKsUdfB6o92XOJ+mjJlSl0RDz/8cMMgWHx3xP0YIfW412fPnp2fN1yef1W1Nxo+WP070Pst75wKFoZL/1bQ1LyIqr+PsoKr+n1VDBVH2Y8++mjdHz7F6OMR8CyOVB7HNQqwxvZOXc9VtTfqWJ6K3ylvvfVW2nrrrcuHWCdAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAkMq0HaANV67GuHU8hRB0QjDRZCnGKSL42JfjL763HPPlU9L5RBIHPvKK6+kF154oXbesssum2KEz+LU28hacVx5RLnYFqO0vfrqq7V5hFUXW2yxukBsHCPAGgr/PUX4cemll85W83kEGeO12hE4LQaU44BygDXCXUcddVR+bixEPzzzzDNpoYUWql0rzUYnjWPnhQBrXLsRni7fE+EbIdO+pvKod9nx0QcxutoyyyzTI/zbbPTV7Nziq69jW/Rp3HMxj5Bkua7ZMTECc6en4ui/jzzySI8gYfHz435dbbXVapt6C9h24nlQrEcsRz/9+Mc/bmiXHduojsWwUXZczOM5GCNT/6//9b9qz6pyn0R4tRhiLp5bxXKnAqxRt8Hoj3YNyq8jz8qL++5vf/tbrZ/j3o7gfjaVA6zD6flXRXszh8Ho34Heb1kdq5gPp/6tor1RRie+j6LcqgKd2267bY8RrLPflHGvloOr8dkxNQuwxr5OXM9VtTfqV56K3ykCrGUd6wQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQDQJtB1ijEYceemj65Cc/2a/2RGjr4osvbhrWKwdY+yo0/kE+wrAxwmGzaamllqqN6rnqqqs2O6ThdgHWepZ4fX0EVvo7lQOscV6EnSP03NcU/frggw+mGG03m+aFAGu0JUbiLF6LEajZcssts2b2Of/Upz5VGwmuHBhudGKEtMO8OCJy+bjvfOc7KcrsbYqR6+Le7U9AtLdyWt3XiQBrJ54HjdoV4amdd9651xDrFltsUXdqMWwUwe4I7PfWz9En06dPT//n//yfunKqXulkgHWw+qNdkxhZfMMNN+x3MeUAa5w4nJ5/VbQ32jxY/TuQ+y3qV+U0nPq3qnZX/X0U9aoy0Nls1PNi+yOwGs/S+EOmmHoLsHbieq6yvcV2xXLxO0WAtaxjnQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgS6QaCSAGs0JF5rfuCBBzYcpTNraITovve976W//OUv2aYe80022SR96UtfSmPHju01uBWhv//7f/9vmjZtWo8ymm2IcnfaaaceI1QWj49yo5433XRTipEpm00x0uWFF16Y747jTz311Hx9oAvl0TCjnLlz59aCcAMts8rzttlmm7TddtulRRddtGmxMSLhjBkzaiOwvvzyyz2Oi6DRpEmTmvbv448/ng477LBam4uvty+H/aLggQYcY+TXrbbaqkfdBmNDtP0rX/lK/lER1I17p5Uprr8TTjghrbTSSg1PizDO/fffnw4++OCG+8sb99xzz9RsRNW4H/bdd9/aiMRZgHWw/DrZv1U+D8qe2fq4ceNqr6yOfmo0unD5mi6Gje699950+eWXpyOPPLLhMyvus+9+97vpvvvuyz6uY/NigLU8ou/48ePTySefnH/2XnvtlZ566ql8/dprr83v9d5GGh6M/sgrNcCF9ddfvxYebzZyYxQbo4VHm6+55pqGnzKcnn9VtDdDGIz+bfV+y+pW5Xw49W9V7a76+6jq31ebb7552n333fPnULHd8cdP8f12+umn579fewuwZudWeT1X3d6sjjEvfqcIsBZlLBMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAtwhUFmDNGhQjdK611lppjTXWqL1O+Y033kgPP/xwmjlzZmoUZszOazRfffXVa+WMGTOm9or5eG12jEoYgbo777yz0Sn93vaJT3yiVs94dX2E/ebMmZOeeOKJFKExU98CETCOUXejv+M18xHaitBavMY8Apn9maIPIvwWo4/FK+ujb2+44YZeR9PtT7ndfkyMmBl+2RQBxbvuuitbbXkeIbP11luvdo/EPXbPPff0uw+KH7bkkkumeA123L9vv/12rU9+97vfpdmzZxcPmyeXu+V5UAwbxbMoRnSMKe6zCPdHcPLZZ59Nt956a69/CDDcO6lb+qM3xxgdep111kmrrLJKeumll2rfHxEa7+/9Mtyef+22t2g5HPq3WN+BLA+3/h1IGxudU9X3UaOy290WAefol3iO/vnPf+71j5Ra+ayRcD234uFYAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQikDlAdZWPtyxBEaawHLLLZfOP//8vNmvv/56bUTbfIOFES3QLMA6olE0ngABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBeVJAgHWe7FaN6laBE088sTbCaVa/66+/Pp199tnZqvkIFxBgHeEXgOYTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQGEECAqwjqLM1degExo4dmyZNmpQ22mijvBLvvfdemjBhQr5ugYAAq2uAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGRIiDAOlJ6WjsHXWCFFVZIZ511Vho1alQaPXp0j8//zW9+k04++eQe220YuQICrCO377WcAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwEgTEGAdaT2uvYMm8MEPfjD98Ic/bPh59957bzrkkEMa7rNx5AoIsI7cvtdyAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAiNNQIB1pPW49g6aQKMA62uvvZbuvPPOdNJJJw1aPXzQ8BEoBljvuuuudOSRRw6fyqspAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEWhAQYG0By6EEWhX4+Mc/Xjvl3XffrQVXWz3f8SNLYIMNNkjzzTdfrdF33HHHyGq81hIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMKIEBFhHVHdrLAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBg6AUEWIe+D9SAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDCiBARYR1R3aywBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYOgFBFiHvg/UgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwogQEWEdUd2ssAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGDoBQRYh74P1IAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMKIEBFhHVHdrLAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBg6AUEWIe+D9SAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDCiBARYR1R3aywBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYOgFBFiHvg/UgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwogQEWEdUd2ssAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGDoBQRYh74P1IAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMKIEBFhHVHdrLAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBg6AUEWIe+D9SAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDCiBARYR1R3aywBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYOgFBFiHvg/UgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwogQEWEdUd2ssAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGDoBQRYh74P1IAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMKIEBFhHVHdrLAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBg6AUEWIe+D9SAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDCiBARYR1R3aywBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYOgFBFiHvg/UgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwogQEWEdUd2ssAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGDoBQRYh74P1IAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMKIEBFhHVHdrLAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBg6AUEWIe+D9SAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDCiBARYR1R3aywBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYOgFBFiHvg/UgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwogQEWEdUd2ssAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGDoBQRYh74P1IAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMKIEBFhHVHdrLAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBg6AUEWIe+D9SAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDCiBARYR1R3aywBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYOgFBFiHvg/UgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwogQEWEdUd2ssAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGDoBQRYh74P1IAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMKIEBFhHVHdrLAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBg6AUEWIe+D9SAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDCiBARYR1R3aywBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYOgFBFiHvg/UgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwogQEWEdUd2ssAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGDoBQRYh74P1IAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMKIEBFhHVHdrLAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBg6AUEWIe+D9SAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDCiBARYR1R3aywBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYOgFBFiHvg/UgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwogQEWEdUd2ssAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGDoBQRYW+yD008/PS288MK1s2688cZ05ZVXtliCwwkQIEAgBAbzeXrKKafk6P/2b/+Wbrvttnx9sBbWXHPNtP/+++cfd84556S77rorX69yoRvaW2V7lEWAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDDvCQwowLrRRhul7bbbrjKNs846K82aNauy8jpZ0L//+7+n+eabr/YRjzzySJoyZUrLH/fNb34zLbfccnXnRaDqvvvuq9tWXhk7dmzabbfd8s0PP/xwuuSSS/J1C9UK/PM//3Padttt0zLLLJMWW2yxvN/feeed9MYbb6TZs2enm2++Of3qV7+q9oOVRmCECFTxPO0v1S9+8Yv80AivHnvssfn6YC18+tOfTgcddFD+cddff306++yz8/UqF7qhvVW2R1kECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLznsCAAqwRwPzCF75Qmca0adPSjBkzKiuvkwVVEbi69tpr0/zzz19XzaeffjrtvvvuddvKK7vuumuaOHFivrk/5+QHW+i3wKqrrpq+853vpA9+8IP9Oue9995LJ5544rC5hvvVqBFy0PTp09OCCy5Ya+0dd9yRzj333BHS8mqa2a5fFc/T/rakGwKdAqz97S3HESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIjAQBAdYWe7mKwFWjAGtUI0bmmzlzZtMaCbA2palsxworrJDitd7lgHFfHxDBx7g2TMNL4LrrrkujRo2qVfrBBx9MBx544PBqwBDXtl2/Kp6n/SUQYB38EWf72zeOI0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQGJkCAwqwTpgwIW211VZNxZZccsk0evTofP9rr72WXn311Xy9vHDKKaekBx54oLy5K9erCFw1C7A+8cQTafLkyU3bLcDalKayHRdffHFaaqml6sqbNWtW+n//7/+l2bNn1/atscYa6UMf+lD68Ic/nIcfBVjryIbNSrsBzGHT0A5VtF2/Kp6n/W3ahRdemAfTb7zxxnT++ef399TKjhvMEVi7ob2VwSmIAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgnhQYUIC1L4kjjzwy/dM//VN+2GWXXZYuvfTSfH04L1QRuGoWYA2XfffdNz322GMNiQRYG7JUtnHzzTdPe++9d17eu+++m6ZOnZoiwNps+s53vpM22mijdMYZZ6QIxZmGl0C7Aczh1drqa9uuXxXP0+pb1bkSBzPA2rlWKJkAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBANQICrC06VhG46i3A+tBDD9VCk42qJcDaSKW6bd/73vfSRz/60bzAM888M/3qV7/K1y3MewLtBjDnPZHWWtSuXxXP09ZqPLRHC7AOrb9PJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6C6Brg+wrrnmmukf//Ef0/jx49PKK6+cYlTMp556Ks2cOTPdcsstac6cOS2LrrDCCmn99ddPH/nIR9Kqq66all122fTGG2+kZ599Nj3yyCPpJz/5SdMymwWudt5557TBBhukJZZYolbHJ598Mv3nf/5nuvrqq3uUVQ6wvvXWW2nBBRfMj4ug6l/+8pd8PVsYSID1i1/8Ylp77bXTuHHj0iKLLJJeeuml9Oijj6Z77703/fa3v82K7jFfd9110xprrJFvb9SOfGdhYezYsXWj7zY7b7nllksx4mnUa+mll665/dd//Vf685//XBvx9L777kt33nlnoeTOL15++eVpscUWq33Qe++9lyZMmND2h2666aZp9OjRtXLuvvvu9PTTTzctM8zDL6Z33nkn/frXv+5x7DbbbJNve/jhh1M4xTkbb7xx7T6JazkMo39/97vfpdmzZ+fHN1qourzyZwz0+iuXE+vFusZoty+//HLtsLjvJk6cmJZffvnaNf7KK6+kZ555Jl155ZXpgQceyIsqnp9t/PrXv54t1u7/66+/Pl8vLvzpT3/q1/X42c9+Nn3gAx8onpqee+65dOutt9Zt6/aVeEbuuOOOKZ5jV1xxRa26nfCr4nnayHK99dZLyyyzTKNdtW2PP/54euKJJ5ruz3bEMyB7Nv/+97/P76dWnvdZWTFvNcAa1/V8882XF9Hsedqt7c0r/v5C/HFAjFYd30fxPRnXVjzj43to7ty5tefYhz/84dopcQ8Xn/8xMnZ8T5533nnFIi0TIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgMc4GuDrBOnz49D/Q1co6QYYSrLrnkkka7G27bb7/90uc+97mG+7KNUe4f//jHdOCBB2ab8nk5cDVt2rQU9VxggQXyY4oLESbcY489iptSOcB6zTXXpK233jo/JoKvhx12WL6eLbQSYI2g1NSpU9P888+fnd5j/uqrr6ZDDjkkPfbYYz32lT8rgn1nn312j+PKG0499dS0+uqr55sPP/zwdM899+TrEYKL/8ohv/yAwkKEiqN+s2bNKmzt3GL0QxZWi6D0lltu2faHla+XKVOmNC3ztNNOS6uttlptf7MA7S9+8Yv8/Aip3n///Wn77bfPt5UXrrrqqnTBBReUN+frVZeXFdzu9ZeVk83/4R/+IZ1zzjnZarrppptSXGsRNo/garMpQsNHHHFEbXexrc2Ob7Y9wo6TJ09utjvf3ugzIpwXgcfhMP3Lv/xL7Vk0ZsyYWnWLz6JGbetvm5r5le+PgTxPG9Wh/IwtH3PbbbelY489try5x3rV9WslwPrjH/84rbjiinmd/va3v6VvfOMbDf+4oVvbm1X+uOOOSxGybTTFs+7II49M8WyMP2aI6bXXXktf/vKX88Mvuuii2r44NoL7l112WV3ANT/QAgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwLAS+P8AAAD///sP5e8AAEAASURBVOzdC/xVc77/8U+dVFRKN8lRaYoUGWQMf9PhuD3ooothpOSWlHSRolzLrYbESCa3LiSiOG7HHDpmJuMoBlN0UZIuhK4iUpP/fNaZ7zrfvX5r7b1/e33X77f27/dajwd7Xb/r+32uy9799nt/V5UmTZr8JI6Hm2++WY499li/1FmzZsnMmTP96Vwjv/rVr+Tqq6+WvfbaK9eq3vKVK1fK0KFDs67bqlUrGTNmjNStWzfrevbCgQMHypo1a+xZ8sILL0jVqlW9eevXr5cGDRpIzZo1M9YJTnzxxRfSr18/f/bzzz8v1apV86evvfZaGT16tF+3n376Sfr06SNbt27119GRSy+9VLp37+7PC5ZrFgwbNkxOOeUUM5n1Vff18MMPe+2yV1Qn+5hpXXr37m2vEjpu++zcuVN69uyZsd6ECRPk0EMPzZiXbULrN3XqVJk7d2621Zws0/ba58f1118vf/vb32KVbXvkOk/vvfde0fNUB213ly5dSuz7pZde8uepb40aNfzpqJGFCxfK2LFjQxe7Lk934uL8C1b2wAMPlClTpviz33zzTTnqqKOkVq1a/rywka+++kouueQSb5Hd1rB1s81bvXq1DBo0KNsqkfvYtGmT9O3bN+e25bWC2ur95rjjjitxz/3ggw/khhtuiGxbvnWO8rOvj0Lvp2F1CN5jg+u8/fbbcttttwVnl5h2XT99b9P7vRlefvllefDBB82k/zpt2jRp2LChP71jxw7R96ONGzf68+yRtLZX66jvLwcccIBd3RLju3fvlj179kj16tW9Zd99952cd955/nozZsyQ+vXr+9M6ouv88Y9/DPXLWJEJBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgdQKVElbgHX//feXRx55RKpUqeKjabBl+fLlsmrVKi8s2rp1a2nWrJm/XEfmzJnjBR0zZloTzzzzjOy9997WHJHNmzfLZ599JhquatSokTRv3lw0zGUCqrkCrHZhGpT75JNPvG3btWsntWvXthd7ob4VK1Z484JhIw006b51f2b4n//5H7n99tvNpPeaT4D16KOPLhFW/OGHH2TZsmWiQTptn4Yk7QCt+nbt2jVjXzrx0EMPSdOmTf35/fv3Fw2ZRQ0auNR1zKAhw3HjxplJ79UOsOp+N2zYIF9++aWsXbtWNMSk+9NgYjCYOXjwYO/4ZxTmeMKumxatbhreU7tCBzsA5zrAatfp888/l6VLl3puev7tt99+9mLRtr3xxhsZ83QiKtRZaHkuzz+7ssEAq5475jrV9Xbt2uVdzxr006Dbvvvu691D7ACrBmuDITg918yg2+p9JmxYvHixzJ49O2xRxrwwz7QGWPWaP/vss0XvuVGDfQ0n4WdfH3YdSnM/tbcz4/pjhTp16phJ71wx4XCdWUiA1S/sHyOF1i+fAOvjjz+ecf1+8803MmDAANm2bZtdhYzxtLZ3+PDhcvLJJ2fUVe2WLFnizTv88MMzgrpmxWCANfheZNbTVw3762eDp59+Wt566y17EeMIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACKRdIXYDV7oVS7TTsMmrUKC/kaFtqD6NDhgzxQ2waaNNeBMNCPhoMPeuss/zNNfCiPdxp6DU4aA+c2oPsIYcc4gVKs/XAqttGlTVp0iRp0aKFX/yiRYu8XlZ1RliA9aOPPvICOKZHybBQaT4BVg3//iOU7O9XA6KXXXaZP60jGljT+tmBXjuoZlbW3lMvvvhiMymvv/666PGJGiZPnpwRLA4Lnd56662e7auvvpo1cKzr2eFCDRDqeZDk0KlTJy8oFtyHhmv//Oc/yyuvvBJ6fgXXt6ftgF5SAdaw8PYdd9wh7du396ui15HpidSf+Y+RsMBlnPJcnn92PYMBVrNMQ8/33XdfiXCu9l45cuRIL0huB8PNdub1xRdf9MPyGgAeMWKEWVTQa5hnmgKsGvy/6KKLRIPGdojdbqzeezRgqIHA999/315UYjyun319aOGF3E9LVCpihn1sCg2wxq1ftgCrvvdob6wavjbDli1bvPc1M12a1zS0N/hep/dQfZ+wB+19/IQTTrBneb2r2j2w6sJTTz1VunXr5v3Yw/6Bi72h/uhA79UaAlY7BgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIF0C6QqwNqhQwe55ZZbfDENo5xzzjn+dHBEH2n/m9/8xp+tva9pcM8eNMj22GOP+UFXXab7ePfdd+3VSoxroCasN7dg4CoqCBUM3NkBwmCoR3tg1QDrueeeKxdeeKFfl3nz5snEiRP96VwBVg18avDTDNorZffu3c1kxqs+0lkf7WyGsMCsLrMDat9++22Gt9nWvNrraq+BvXr1MosKep07d67/SOlsbSmo8IiNfv/738u//uu/Riz9315Ztbc/DXkuWLAgcj2zwD5fkgiw6nljP5Lc7Fdfn3zyyYwwnAbFNEhtD3bITefHKS+J88/UNXg96Xw9J/r16xf5WHWzbbZX+5ytyAFWDaNrD8n2Y+mDLtoTsgYMw4L9wXXNdFw/+/rQMgu5n5q65Hq1z/Wo/QTLcF2/qABrgwYNvGCn+QGD1sN+zwjWK5/p8m5v8Icj2e5/M2bMyOgdOdgDa7C9+l747//+76Kh36hBf3yiIew//elPUaswHwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKCcBVIVYA0+wn38+PEyf/78rETPPvus1KxZ01snLPCjvU726NHDL2P9+vUZj7n3F+Q5Egw09e3bV7SHxbBh1qxZ/mOs9fHkGlDVISrAqss0PFajRg0dFe1dUnucM0OuAOs111wjJ510klldwnpV9Rf+YyT4WGbtyfK1116zV/F6XLUfvT106FDRIFJw0N7ytAdcM4T1tGeW5fsa7MW2c+fO+W5a8HoaiLr99tszes+NKkwD1s8884wXkopaxz5fsgW4dHu792Ht6VEDh8HBDqXpsuuvv17+9re/BVfzpvv3759RRtj54LK8JM4/07CwAKsGnDWcHmeIG8AM7jvoqcvLqwdWvW41EK898Ub1tvr99997QX0NO2uAtbRDXD/7+tB9F3I/zbfO9rEpNMAat35hAVY9jx944AH/fUzb8/nnn8vll1+eb9NC1yvv9uq12bhxY79u2X44cv7558sFF1zgr5srwGpWbN26tfdDCQ3PR53jO3fulL/85S9er6xff/212ZRXBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgRQIpCrAqo/93W+//XyWadOmeY+UNjPsxwZXrVrVm921a1epV6+eNx4MfOrM4KPodTqfnjO9AkP+ZweucvUQaz9O3a5btgDrxRdfLNpbohk0IDZlyhRvMleA9e6775Y2bdqYTSUqbGpWCPb4+txzz8mjjz5qFnuv+thmLccMYSFIXaa9uWqvrmbQx5Rv3LjRTGZ9bdmypfdY6CZNmviPc9cNNIzbtGlTf9uyCLCanZ199tle77/2+WiWBV+XL18uw4cPD872pu3zxXWAVYNZ9rkSrIDWXa8pM3z88cdy9dVXm0nv1Q65xS0vifPPVDYYYLWvJ7NOIa9xA5jBfWpY1H4EvC7fvHmzLFmyJLhqotO//e1vpW3btqH70N6Wly1bJrNnz87ZE3VoAdbMuH729VHo/dSqTtZR+1wvJMDqon7BAKu+F/385z/3f7SgDVi9erUMGjQoa1vyWVje7bV/jPHjjz9m/JAkrP52ffMNsNrlnHHGGaL37WbNmtmzM8bt99OMBUwggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIlItAqgKswWBnISLBHvKCwcq4IUg7cBXW46td56geNYPt1EfA66PbzaBB0r322subtIM/uQKswR7vcrX1uOOOkxtvvNHsVhYuXChjx471p82IXd+oEJcdPsrlouVqj3vau6z9yGyzv6hX7eF1y5YtUYsTmd+oUSMveKU9/GnANqqXv6hgr32+uA6wajBSe9jMNtgBQw0Ua7DYHuzjFre8pM4/rW8wwBrWFrtd+Y7bPkuXLpURI0bku2mq1ws+kl0rq9flq6++6gVXXVU+rp99feS6b0TdT/Nti32uFxJgdVG/YIA1rO6uQpbl3V773Ni6dav07t07rLn+PPt9ppAAq1/QP0a09+l/+7d/KxEm/+CDD+SGG26wV2UcAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoBwFUhVgtQM3hZoMHDhQ1qxZ42/+7LPP+o9m1p4HtcfWOIMduFq1apUMHjw4sriowJUd1NGNgwHWK6+8Us4880y/3FmzZsnMmTMlV4C1tG3VnuomT57s7yeq57877rjDewy5WXHUqFGyePFiMynBXmNNff0VrBHtbfWuu+7K6HHQWpx1VPdT3o+A7tChg3Tp0kWOPvrojN5if/rpJxkwYICsW7cuow32+eI6wKrnuZ7v2QY7DB0WPravubjlJXX+afuCAdZsvd5m8wgus0N2FT3AquHvefPmifZs7WqI62dfH4XeT/Nti32uFxJgdVG/fAKs2p7ge0K+bbTXK8/2NmjQQKZPn+5XZ+3atd790Z8RMmLfP+IEWPfff3/vBxL/7//9P9l7770z9kSANYODCQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoNwFUhNgDYYpd+3aJR9++GGpgfQx5tu2bfO3s8OiuR6R7m+UZcQOXBUaSLTrpLsKCyvZ65jgYa4Aq72NBio1aJlrsENOUT0MaljT7pn1nXfekTFjxvhFa1BJA0s6ZNtv3bp1RXvprFGjhr+tjmj7Nm3aJNpL344dO/xlbdq0kTp16vjTaQiwmspoEHfChAl+T7k6/5VXXskIBOs8F+eLlmMG+3jlE+KcO3euVK9e3dt89+7dXq+3pix9dVleUuef1jMYYJ0/f76MHz9eF8Ua4gYwY+08wY31+jzmmGNC96DXqIbV9dx44403QtfJd2ZcP9fXR7Z62+d6IQHWQu/3dp2iAqzff/99RtjS3PPtbUs7Xp7tDb6ff/rpp3LVVVdlbULcAOs555wjZ511ljRu3Dh0P3re6z7sYG3oisxEAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQKDOB1ARYtcV24Gb79u1eL2pxJZ566impXbu2V0y2cGW++3ERuLKDfrrfsADryJEjpWPHjn61Hn74YWnYsKF0797dn/fFF19Iv379/OnStrVVq1aivcSaYcWKFTJs2DAzmfE6Z84cP3j6448/So8ePbzljRo1kqlTp/rrZuvFUwNMZ5xxhr+uBop1/xpGDBs0oNiuXTt/UZoCrFqpnj17er3Pmgp+9NFH3rE00/pamvPl/vvvl4MPPtjbPOpcta+R9evXe4/KtvcXHLfPNQ0Hn3vuuRmruCwvyfMvGGDVnkQnTpyY0ZZCJuIGMAvZZ1ltoz1R9unTR44//nj/2g3uW38o8N5774n2mqwBzdIOcf1Kc31E9Widb53tcz1NAVY1nDJlijz99NNSq1Ytvzn5BNT9lUNGyru99v43btwoF110UUgt/2+W3Vt0vj2wao/Y5513nhx66KFStWrV/yvMGtPPEq+99pr34wlrNqMIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACKRBIVYDVDklqsMoOaxZqNXnyZNHe4MyggS59lHahg4vAlR0q1HqEBVh1vh0O0xDO66+/nmESDLA+9NBD0rRpU93UGzp37mxGQ181IKtBWTO8+eabMm7cODOZ8XrdddfJiSee6M/THlkXLlzoPcJee70zw4MPPigvv/yymcx4nTlzpmgvrDpoQHPIkCGij+WOGrS3Vrs3vbQFWLUt2iYzhPVga58v2uvloEGDzOolXmfMmCH169f35ucTYNWehi+44IIS5dgz7BDZl19+KdqLrz3Yy+OWl+T5R4DVPmqlHz/55JPl17/+dca9MFiKHv///u//lkcffTS4KHLavkctXbpURowYEblu2AL7+nDRw2nYPsw8+1xPS4DVrof2OH3XXXdJlSpVTJW9Hwfo+2IhQ3m31w6khoXng22y65stwHrAAQdIr169vGB2zZo1g8V403v27BE9H5944glZvHhx6DrMRAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEECh/gVQFWIOBxVwBzHz4rr/+ei/oYtbVfeijswsdXASu8g2w3nzzzXLsscf6VdXHMJseOnVmMMB6xx13SPv27f31b731VlmwYIE/HRzp37+/dOnSxZ+tPWhq4CdsaNmypfzud7/zF2koaNSoUV6A04RSNTTUtWtXf53giG23efNmufDCC4OrZEzrcapevbo/L20BVq2YHboKHg9dbrdh06ZN0rdvX50dOtgB7nwCrLt375Zu3bqFlqUzgz3sLlmyJCOwrOvY9Y9bXpLnX1kEWLP1QKxW+Qy33XabVKtWLWPVd955RwoNIWYU5GCiQYMG0rt3b9FH2UeF//TcW7t2rWgAUXuuzDbYAdZC/Ox7QmUMsGrYX0P/ZtCAuf3DDb2nauhde7Yu7WBf23ZQNls5Lo+H/YMF3We2H48cffTRoj+KMENYgPX888+X0047LeNHDWZ986oh7D/84Q+iPwZgQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEi/QKoCrBq4POqoo3y1bD2C+ivlGDnnnHMyHl2cT09w2Yp0EfDJN8DaqFEj77HHdo98dt2Cgckrr7xSzjzzTH+VXI+g1seG16lTx19//PjxMn/+fH86OGIHkjTsOHjwYNEebs2QqwdGO+wWrLspw7wGj5vOT1uAVcNU2ousGTQsrOewPWgguF69et6snTt3Ss+ePe3F/rj2vnj33Xf70/kEWHXlbD3e3nDDDfLLX/7SL3PevHkyceJEf1pH7JCbTscpL8nzL6kAq91LZFgPumpSmiHoqdvmCi6XpnyX62qI9dxzz5UWLVpk9Ppp70PDt2PGjLFnZYzH9XNxP82oUJYJ+9iUR6BTq6bm2uO2GYIBVp0f7Mk4n56RTXn2a3m3V99P2rVr51dJexC/9957/Wl7JLhuMMAa/HGLva2GfD/88EMvtLps2TJ7EeMIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACKRdIVYBVQ2q///3v/TCVhvguv/xyr6fRXI7aa52GqcIGu2dLXR4WGgpup2G8Bx54IDhbXASu8g2w6s7HjRsnhx9+eIl66IxgCHT//ffPePy3+l1xxRWyfv36Ett37NgxozfObOFKs/HAgQPlrLPOMpOyYcMGadKkiT+tPXC+9dZb/nRw5Omnn5ZatWp5s3P19hk8ZrpR0gFW7a23bdu28vjjj8urr74arH6J6WeffTajF0vt9W/27NkZ62nAt1mzZv68qF5x77vvPvnZz37mr5dvgHXjxo0ZAW2/gH+M2OFCnd+vX78S15IdctN14pSX5PmXVIDVDhjn6kFYfXINQU9dP60BVtMW7UH5ggsukJNOOkn22WcfM9t7/eCDD0SD0FFDXD8X99OougXn28cmzQHW/fbbT6ZOnZrRk+/ChQszeigNti1surzbG+y1W+/5Q4cOldWrV2dUV0Ou+j5n/1AjGGDVe2v9+vUzttNevP/zP/9T9IcYDAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALFKZCqAKsSXnPNNV6QynBqqExDgWGPttdQm4YaO3To4IV9tHc37eUtOJx66qlecMae//HHH8vVV19tz/LGtayRI0d6QS4NbAYf3ewicFWaAKuGH+1eTu0KBwOsuuzOO++UI444wl9t165dXnv08d5mUA/tOdQODKlxrscuN2zYUKZNm2aKyXj98ccfpUePHhnzghP333+/HHzwwf5sDTLp47HtQQOkGvKsUaOGPdsbTzrAqgHc9u3be/vavn2799h3DbqtW7cuoy4a4u3bt68fxtWFep5qQPTLL7/MWFd7qT399NP9ed9//72MGDEiI8QVFlLON8CqBYc5Pvnkk7Lvvvv6+416vLsdcjMrxykvqfMvqQCr3jNatWplmu6FsrU3SPt68RfmMRLmmfYAq92sX/ziF6KPalcTvT/kCrDG9XNxP7Xrn23cPjZpDrBqG0455RQZNmxYRnMmTJggb7zxRsa8bBNpaG8wwK8hVu1h9pVXXvGq3qlTJ+nfv79UrVo1oylRAVbdftGiRd571cqVKzO2YQIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECg+ARSF2BVwrDeNzW48u2334q+VqtWTWrXrp3RQ51uFxVg1WX6aPSDDjpIRzOGHTt2yA8//OCFtbQnQjtIk4YAq1Y2GBIzDQgLsAZ7wTTraju3bt0qDRo0KBEOzaf3VVNO1KOccz1qXLfXcPAtt9xiivJe9XhqT641a9b0QsPBHiDtlcsywGrvV8OkGqjSc0PraZ8jZj3tMVHP27DhxRdfzAgLa3kakNVXDZnaQWKzfWkCrLqNBmi/+eYb+Zd/+Rfv2giWqWG4sFCmHXIz+45TXlLnX1IBVu39UQOrYYOem2ZYunSpjBo1ykxGvoZ5FlOA1W6Y9n69atWq0B8FmPXi+rkOsOp5MnHixBLXqF4PdiherxcN3QcHfQS93eOs6/r96le/kmuvvdbfbbbewMeMGSPHHHOMv66ej5dddpnXS7KZmfb2tmnTRn7729+WOB6m/lGvwQCr9ty6du3ayHtsVDnMRwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEi3QCoDrPoIZe2Fs0WLFqXSyxZg1YJuvPFGOe644/IuMy0B1tatW3uhrGDFwwKsus6JJ57o9WSrQd9cg4aCR48e7QXVcq2ry3v16uX9F1xXe87V8FeuQfd1wgkn5FrNC5dpaPDII4/01y2vAKtfgYiRXOHd6667zjsmEZt7sz/55BMv0Gp6A80nwKrBX+0VN9tx1nK0F0R91HbYYAcuXZSn+0ji/EsqwKr1vemmm0R7Hs02hPVKG7a+7WmWF2uA1dQ/12scP9cB0bCQfK7628s1JDlgwAB/luv6lSbAqpV46qmnvEC6qdBXX30ll1xyiZkM/VGAvzCPkaTbq1XQ9y8NiVevXj2yRnp96Q9I9L1fh2CANXJDFiCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggUtUAiAdaRI0dKx44dfZhZs2bJzJkz/el8R84++2y58MILM3rOC26rPelpL4Hz5s0T7eky16A92mnYsk6dOpGrbt68WZ5//nmZO3duiXUKDTRp73ndunXzynvuuedkr7328sseNGiQaIAn2xDWg2xUgFXL0Z5W9dH0BxxwQGixGmz88MMP8+pVMlhAsEfR0oaNzjnnHOndu3dk8PLTTz/1ekHs27evnH766f7uO3fu7I8nNdKzZ085+eSTpVmzZjl7DdTw13333ZdXcFcfk92lS5fQauv5O3jw4Iyedu3zxd7IDkjqo7Q14HbzzTeHXiPas7D24rh48WK7iIxx1+WZwl2ff1re9OnTTfHe9a49bboaTj31VO/6bNq0qXdtBnuwXb58uQwfPjzn7oLXhm5Q0QOs2sZC/VzcT3X/ZkhLgDXq+v3lL3+Z0cNrth5YtU0a/rznnnsyemmeMWOGzJ4922ty2ttrjosG7fX6Ofjgg/1ArhppaH7BggWiPVhrm0wP3MGgrimHVwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGKJZBIgDUJIg3+tG3b1nuEu4Yv169f74U+NcRX6KC9ex5++OHSvHlz2bp1q1eehjrXrFlTaJGp3E5Du+3bt/fstm3bJu+//75o76blPegx1UdMa++a27dv98JMr732mmzZsqW8q+bt/4gjjpCDDjrICwE3atRIdu7c6Z0bGjZeuXKlqGVphnr16nk9Jup5vGvXLq/Nf/7zn0t1vgUDp9qjrQ7777+/nHLKKV4wW8Nfb731lnz55Zc5q+e6vLAdpvX8C6sr8xBAIHkB/YFA8H3WDjN/9NFHcu211yZfEfaAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggUK4CRRNgLVcldo5ASgSiAqeFVs91eYXWg+0QQKDyCuiPGKZMmeID6A8ZtHdrBgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGKLUCAtWIfX1pXwQRcB05dl1fBuGkOAgiUgcCdd94p2uO1GSZOnCjz5s0zk7wigAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIVFABAqwV9MDSrIop4Dpw6rq8iqlOqxBAoFCBOXPmyMqVK+Wpp56S999/v0QxAwYMkE6dOvnzt27dKr179/anGUEAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCouAIEWCvusaVlFVDAdeDUdXkVkJwmIYBADAH7HrNz507ZvHmzbNy4UerUqSNNmjSRmjVrZpQ+duxYWbhwYcY8JhBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQqpgAB1op5XGlVBRWww2CLFi2S0aNHx2qp6/JiVYaNEUCgwgnY95hsjfvpp5/klVdekQcffDDbaixDAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQqEACBFgr0MGkKRVfwA6D/fWvf5Wbb745VqNdlxerMmyMAAIVTmDy5MnStGlTqVatWmTbtFfWW2+9VVasWBG5DgsQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEKp4AAdaKd0xpUQUW6NChg1StWtVroYvHbLsurwLT0zQEEIgh0LJlS2ndurU0atRI6tevLxs3bpSPPvpI/va3v8UolU0RQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEilmAAGsxHz3qjgACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCBShAAHWIjxoVBkBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAoZgECrMV89Kg7AggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggUIQCBFiL8KBRZQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKCYBQiwFvPRo+4IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAEQoQYC3Cg0aVEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgWIWSF2A9b777pO9997bM3399ddl9uzZxexL3YtMgPOvyA5YKat72GGHybBhw/ytpkyZIn/961/96WIauf7666V58+ZelZctWyb33HNPMVWfuiJQaoGWLVvKbbfdlnW7Bx98UObPn591HRYiEBTgfhoUSf80n9fSdYy4P6freFCbdAkU+/XRqVMnadu2rRx44IGyzz77ZOCOHDlStm7dmjGPiXgCZfn+NmHCBL+yzzzzjLz99tv+dEUcqWztrYjHkDYhgAACCCCAAAIIIIAAAggggAACCCCAQMUVKCjAesIJJ8i5557rTOWBBx6QFStWeOW98MILUrVqVW985cqVMnToUGf7oSAEcglw/uUSKu7lv/rVr+Taa6/1G/Hyyy+LBt6KcZgxY4bUr1/fq/p3330n5513XjE2gzojkLdAu3btZPz48VnXnzVrlsycOTPrOixEICjA/TQo4maafy+4cSyGUrg/F8NRoo7lJVCs14fWe/To0VK3bt1IumuuuUb0h3QM7gTK8u8RL730kl9xDa/m+qGYv3KRjlS29hbpYaLaCCCAAAIIIIAAAggggAACCCCAAAIIIFBJBQoKsF511VVyxhlnOCPTQIrpMa0s/2DvrAEUVGEEOP8qzKEMbQgB1lCWgmdOnjxZqlev7m2/cOFCeeihhwouiw0RyCVQrAGQXO1iefkLEGBN5hjw74VkXNNYKvfnNB6V/6tT2j+vpb1+/ydZ2FixXh9z5syRGjVqZG00AdaSPHHP57L8e0RlC3RWtvaWPDuZgwACCCCAAAIIIIAAAggggAACCCCAAAIIpFeAAGt6jw01KweBsvzCqByaV+l3SYDV7Snw4osvSpUqVbxCly5dKiNGjHC7A0pDICDQqFGjjDmHH364DB8+3J9HD6w+BSOlECDAWgqsUqxKgLUUWBVgVe7P6T2Iaf+8lvb6uTiyxXZ99O/fX7p06eI3fdeuXbJgwQL5/PPPZffu3f78J5980h9n5H8F4p7PZfn3iMoW6Kxs7eWaRAABBBBAAAEEEEAAAQQQQAABBBBAAAEEikmgoACrfpnRrVu3yHbWq1cvo7cOfbz1t99+G7n+hAkTZMmSJd7ysvyDfWSFWFBpBTj/KvahJ8Dq9vjG/YLWbW0orTIKHH300TJ27Fi/6QRYfQpGSiFAgLUUWKVYlX8vlAKrAq7K/Tk9BzXtn9fSXr8kjmTar49JkyZJixYt/KZfeuml8uWXX/rTjEQLxD2fy/LvEdOnT5dq1ap5jXn99ddl6tSp0Q2rAEsqW3srwCGjCQgggAACCCCAAAIIIIAAAggggAACCCBQiQQKCrDm8rn55pvl2GOP9VcrTaCkLP9g71eQEQT+KcD5V7FPBQKsbo9v3C9o3daG0iqjQNoDIJXxmBRjmwmwls9R498L5eNeVnvl/lxW0rn3k/bPa2mvX27h0q+R9utj5syZUrduXa9h27ZtkwsuuKD0jaykW8Q9n/l7RCU9cWg2AggggAACCCCAAAIIIIAAAggggAACCCBQyQUIsFbyE4DmZwrwhVGmR0WbIsDq9ojG/YLWbW0orTIKpD0AUhmPSTG2mQBr+Rw1Aqzl415We+X+XFbSufeT9s9raa9fbuHSr5H26+O5556Tvfbay2vY+vXrpX///qVvZCXdIu75zN8jKumJQ7MRQAABBBBAAAEEEEAAAQQQQAABBBBAAIFKLlA0Ada+fftKhw4dvJ5A9uzZI2vXrpUPPvhA5syZU6pDeNhhh4l+YdSmTRs56KCDRMtat26dfPTRR/Lmm2+KfkFTlsOpp54qNWrU8Hb53nvvyRdffBG5+yOOOEKaNWvmLd+9e7f84Q9/iFzXLOjVq5cceuih0qhRI9l3333l73//u3zzzTeydetWeffdd+U//uM/zKp5vbr069mzp79PfWSd9u6igx7n7t27S+PGjWWfffaR7du3y4YNG2T27NmyZMkSf5skRqK+MIp7/ql/p06dpGXLllK/fn3vPNZj8fnnn8uKFStk8eLF3vEobZtcH9/S7j9q/bS2t7QBVj0Pq1at6jcz1/3G5fXRvn17Of7446Vt27bSoEED7z71zjvvyB//+EfZtGmTlHXgyr5eDcjFF19sRuWrr76Sl19+2Z+2Rz777LO8zu8zzzxT2rVr510neu3rfeqTTz6RRYsWyZ/+9Ce7yHIb33///eWYY47x7qv6aNWGDRvK999/77V/5cqV8thjj+Wsm6vrQx/PXb16dW9/em6sWbPGG497v7Ib4KK9pjyX14cpM40BkGI6vsbRxavL69e+37j4fJDE/dTl+ey6vS6OZ1mUkUSA1dX9z+XxdW2ZxPmSRHtd3p8LrZ/+2+mQQw7xD0Guz3FmRf33lv00kajt0ni/t88P0x7Xn9dMuYW8lkX9XL4faRtdl6dlurw+tLw4g/150pSj91LzbyD9N/rcuXPNIv/1hx9+iPzs76/0jxGXfvb54+LzgV3PQsbt+pjt415vSf09Qj8L6b8po4ZPP/1UVq9eHbU4cn5a/x5RLO09+eSTvb/T2cBff/21vPXWW/YsxhFAAAEEEEAAAQQQQAABBBBAAAEEEEAAgQovkPoA6/jx42Xy5Ml+DyDBI6IBwMsvvzw4O3RayzEB0LAVfvrpJ3n66afliSeeCFucyLyoLyjCdnbvvfdKq1atvEVaV/2yKWrQL526desW6WZvp8HdcePG2bNCx136HXjggTJlyhR/P/PmzZOJEyd64S8NrkYNGvK96aabohbHnh88HnHPP/1SS//T8HCuQUNwo0eP9gKtudZN4vjm2mc+y9Pe3tIEWB955BFp0qSJ32z9kvbKK6+UL7/80p9nj7i8Pm6//XY58sgj7eL9cb32dfnAgQO9MLQu+O677+S8887z10li5KWXXiq4WP0ydNCgQZHb63EZPny4VKtWLXKdb7/91rs+Vq1aFblO0guGDBkip512Wtbd6PFZtmyZjBgxosR6rq8P1/erYIXjttcuz+X1YZebpgBIsR1f2zHOuOvr1/XngyTupy7PZ9ftjXMsy3pblwHWuJ/X7La7PL52uS7Gkzhfkmqvq/tznPpdeuml3o/ijL3+0ObBBx80k5Gv+m+S1q1b+8tvvPFGef/99/3pNN/vk/y85gPEGEmyfq7fj1yXZ7O5uj7sMgsdtz9PlraMq6++Wj7++OPQzVz7JXH/C614KWYmcT7bx0N/HOfq/e3555/P+m+tt99+W2677ba8W5/Wv0eYBhRLe8POIf2xqvoyIIAAAggggAACCCCAAAIIIIAAAggggAAClUkg1QFW7Q1Ve4moWbNm1mOivZb269cvch398kS/XDGPwYtc8Z8L9IuCoUOH5lrNyfLgFxTZ9ptvgPWSSy6RHj165F2/fMJlrv2CX0BpiPaoo46SWrVqZa239vKo7UtqsI+Hi/NvwoQJXk+N+dZXw29Tp04N7eXGlOH6+JpyXbymvb16L7j22mv9pkYFGaZNm+b1rGlW3LFjhxcY3bhxo5nlv7q+vzz66KOivV5mG/Q80R58TeCzmAOsw4YNk1NOOSVbc/1l2u6HH35Y9Doty0F/ODBmzBiv5+R896sBY9MjqtnG9fXh+n5l6umqvVqe6+vD1NG8pikAUizH19i5eE3i+nX5+cD1/TSJ89lle10c07Isw1WA1cXnNW13EsfXtafL8yXp9sa9P7uoX926dWXmzJn+YdCe3Xv37u1PR43Y7687d+70foxmr5vm+31YGMque7bxXP8mzLZtvsuSqp/r9yPX5QV94l4fwfLiTNvne2nLueaaa7wfbgW3S8LP5f0vWN9Cp5M4n+3j4er9TdvnMtCZ5r9HmGNZLO0NO4cIsJqjyCsCCCCAAAIIIIAAAggggAACCCCAAAIIVCaBVAdY7QOhwUV9lLQ+yk4fMV27dm17seiXJPoo9uCgQTDtSbFKlSr+oj179sjy5ctFe/LTcKz2sBPsmVUfFalBwqQH+wuKXMHZfAOswT+C62P/FixYIBs2bPDa27x5cznooIOkadOmXvOyfVmZlF/wCyg9JuYxhVqpXbt2yebNm0WDg/Xr1/d6MNVjWJYBVvvYF3r+2V9waxv1GGgPnmvXrpXdu3d7x0CDuzVq1LB3J4MHD/bOz4yZ/5xweXzDyo8zL+3t1TBErgDr448/Lvvtt5/P8M0338iAAQNEr6Pg4Pr6GDlypHTs2DFjN9rL9NKlS71zRB+Fq2GM4FAWAVa9x+q1aA967ppBr1W9r4YNixcvltmzZ5dYFPzyXlfQnm61B1P94k7vExqmNEFdXa7XUdeuXXW0zIZnnnlG9t5774z96f3ps88+E71/6iOE9b6q9TX3sVwBVhf3A/v9w65cofcrU4ar9rq+Pkz97NfgOTRr1qyMsJK9btLjru9/SR1fVw5Bey3XxfXr6vOB6/tpUuezq/a6Oq5lWY6rAKtd50Lvf0kdX7tuLsZdnS9l0d7gPaI092eX9XvooYf8f/PoMejfv79oKCxq0Cdc6DpmCHtSRZrv90l8XjMWLl6TqF/wXNN6xnk/cl1emFtwH6W5PsLKizPvuuuuK/Ej0p///Of+3080xL1kyZLQXfzud78Tfdy5PQTbpsviHA9Ttqv7nynPxWsS53NSn//0x3h16tTxm63/ZjFP99GZpemBNc1/jzANLJb2Bi21/gRYzVHkFQEEEEAAAQQQQAABBBBAAAEEEEAAAQQqk0DqA6za4572hqiBUnuYNGmStGjRwp+1aNEi7/HS/ox/jtihT52lX2yPGjWqxGPAtQdAfVyyCR9puKhPnz6hobXgPuJM219QuAiwaq9Cv/nNb/wqaXBM2xs26JfD2nPtli1b5IEHHghbRZLyC34BZXauoc777rtP3njjDTPLe23YsKFoGEWDyxoMS2qwj4fuI+75d+utt8ohhxwir776atZAtK5nhwGjjpvr4+vaMe3tzRZg1WCoPlZ233339Vn02tD7QNTg+voI9hSjQcLp06dn7P7OO+8UDbLaQ1kEWO39mfEXX3zR/3JbQ7YjRowwi/J61R8XNGnSxF9XA96XXXaZP60jep/S+70dIA0Lk2Rs5HBC7zdnnXWWX2LUPUFX0HNIA1l6zYcFWF1fH67vV9oGl+11fX1o/YJDMCRRngGQYji+Qb8400ldv64+H7i+nyZ1Prtqb5xjWV7bugywRt2bK8q/F8wxcnW+JHU+m3rqa5z7s8v69ezZUy6++GK/aq+//rr37xt/RmBk8uTJGT9sDPtRWbHd7+N+XgsQOZ+MWz/X70euywsDi3N9hJXnet5zzz3nP71m3bp1csUVV+S9i6T8XN3/8m5IgSvGPZ+T+Hwf1RQ7PJlvgDXtf4+IaqvOT2N77TqZuhNgNRK8IoAAAggggAACCCCAAAIIIIAAAggggEBlEkh9gDXqD+nBLzA0mBp8tHyHDh3klltu8Y+n9vxxzjnn+NPBkeAf49966y254447gqs5nba/oHARYB07dqz3hbGppAbKNFhWyJCkX/D4af2011UN1IY9pr2Q+heyjX08dPs4519p9z937lypXr26t5ladO/evUQRLo9vicLLeEZ5tDcqwNqgQQPRwEKtWrV8hbB7ir/wHyOur49gcFB7OtLQdtjw5JNPZgRtizHAqoFtDYCYIeqc1+UHHHCAPPzww2bVMuuFVYPzjz32mP/DBq2Avqe8++67fl3CRk444QTR9484Qz7Xh+v7lcv2ur4+oizTHgCJqnd5HN+ouhQyP8nr18XnA9f30yTPZxftLeQYpmEblwHWOJ/Xkjy+rp1dnC9l1d5C789J1M8OlH377bcZP/YLHiN7Xe2Fv1evXsFVSjWdhvu93aZCfnBUqgYXsHKc+rl+P3JdXhRHoddHVHmu5xcaYE3Sz8X9z7VTWHlxzmctz/Xn+7A6mnl2eDLqfdSsa16L+e8RaWyvXSdjTIDVSPCKAAIIIIAAAggggAACCCCAAAIIIIAAApVJIPUB1r59+3qP0Ao7KNrTmnkMmj6++txzz81YzX68oy4YP368zJ8/P2Od4MSzzz4rNWvW9GbnCrAFty1k2v6CwkWAVcNV+sWvGfRLUw1gFTIk6Rf2BVScuhbSvrBt7OOhy+Ocf2HlZ5sX7CWsc+fOJVZ3eXxLFF7GM8qjvWEBVj3vtAdic90rw+effy6XX355VhHX14dep40bN/b3edNNN8l7773nT9sjGtbv0aOHP6sYA6zXXHONnHTSSX4bcvWqGnwEsPbU/Nprr/nbJzESdNZHDtuPFU5in6bMfK4P1/crl+11fX0Yl+Br2gMgwfqa6fI4vmbfLl6TvH5dfD5wfT9N8nx20V4Xx7Q8ynAZYI3zeS3J4+va1cX5UlbtLfT+nET9gj26Dh06VPTfXcHhvPPOy+h5/5VXXvF+4BRcrzTTabjfxw3Ulaa9hawbp36u349clxflUej1EVWe6/mFBliT9HNx/3PtFFZenPNZy3P9+T6sjmaeHZ7MN8BazH+PSGN77TqZ40KA1UjwigACCCCAAAIIIIAAAggggAACCCCAAAKVSSDVAdZcPabaj6fTR89369Yt49g9/vjjst9++/nzpk2b5j0S3syoUqWKGfV72OvatavUq1fPmx9Wpr+BoxH7CwoXAVb9Av/Xv/61X7udO3fK7bffHhmE81cMGUnSL/gFVFlYhzSxxCz7eMQ9/0oU/s8ZLVu2lObNm3uPTrfPQQ3zNW3a1N8sLMDq8vj6O0p4JE3tDQZYFyxYID//+c+lRo0avsLq1atl0KBB/nTUiOvrww7P//jjjxkB1bA62F92FWOA9e6775Y2bdr4TYsKk5gV9AcKF154oZkU/WL90Ucf9aeTGNEeYrUnKTPotJ4zLoc414fr+5XL9rq+PqLM0x4ASdPxjTIsZH6S16+Lzweu76dJns8u2lvIMUzDNq4CrHE/ryV5fF07uzhfyqq9hd6fk6jfqaeeKvo5wwxRP5rR3t6113czXHTRRXk/GSLN9/u4gTrjkdRrnPq5fj9yXV6UWaHXR1R5rucXGmBN0s/F/c+1U1h5cc5nLc/15/uwOpp59r8n8w2wFuPfI9Lc3vbt22c8WUXrunnzZtGnsTAggAACCCCAAAIIIIAAAggggAACCCCAAAKVSSDVAdZcPaDaven89NNP0qVLl4xj9/zzz0u1atUy5pV2IluPTqUtK2x9+wsKFwHWZs2aeb1J2sFI3e/3338vn376qbz//vvyzjvvhPY6FKxfkn7BL6A2btwo+iVxeQ/28Yh7/tltOf/8872Atf2Ient52HifPn1ky5YtGYtcHt+Mgh1PpLW9wQBrWLP1S8cpU6aELcqY5/r6sL/s3Lp1q/Tu3Ttjf8EJ+1wtxgBrsIfEsMC23ebjjjtObrzxRn/WwoULRR9hmeQQDLLkqmO+dXF1fdjngIv7lcv2ur4+omzTGABJ6/GNMixkfpLXr4vPB67vp0mezy7aW8gxTMM2rgKsce9/SR5f184uzpeyam+h9+ek6meXGxV6tsNcuc4rPbbFcr+374lLly6VESNGuD41Y5UXp36u349clxcFU+j1EVWe6/mFBliT9HNx/3PtFFZenPNZy3P9+T6sjmaefc/LN8BaLH+PMG20Xytbe+22M44AAggggAACCCCAAAIIIIAAAggggAACCKRdINUB1lWrVsngwYMjDXMFWO0/UEcWkmPBwIEDZc2aNTnWKnyx/QWFiwCr1kTrfNZZZ2Wt1K5du7xeHdTw66+/Dl03Sb/gF1DLly+X4cOHh9ajLGfaxyPu+af11t6Y7rrrrowePvNtz8UXXxx6bFwd33zrUZr10t7efAKs2t5rr71WPvroo6xNd3l91K1bV2bOnOnvb926dXLFFVf402Ejc+bM8c+rYgyw2j0k7tmzR7T362yDflk6efJkf5V8e8r1NyhgpLR1zLUL19eH6/uVy/a6vD6yuaYpAJL245vNsbTLSnuulOb6jfv5IIn7aZLnc9z2lvbYpWl9VwHWuJ/Xkjy+rr1dnC9l1d5C789J1e+OO+4Q7enODKNGjZLFixebSdHP3T179vSnZ82alfHZzF/wj5Fiu9/HDdTZbU9iPE79XL8fuS4vyqvQ6yOqPNfzCw2wJunn4v7n2imsvDjns5bn+vN9WB3NPPt+m2+AVbdN898jTNvCXitbe8MMmIcAAggggAACCCCAAAIIIIAAAggggAACCKRVINUB1jiBzmBYQgObH374YamPgz4Gb9u2baXeLt8N7C8o4rQ3uL9OnTp5PZruvffewUUZ07t375Zx48aJfmFhD0n7Bb+Amj9/vowfP96uQrmMuzweGqLRXmjsx9Nro7TXp02bNon2srljxw6/nfo49Tp16vjTUQFWXSHu8fV34nCkGNobFWDVHortayWqZy7D5fr6aNGihUyaNMkUL2vXrpUBAwb402EjTz31lNSuXdtbVIwBVrsntLAetMPabH/pmE/PaGFllGaeXcedO3dmBFtKU46um8T14fJ+pXV01V7X14fWLWpISwCkGI5vlGEh8+1zxfX1G/fzgev7adLnc9z2FnL80rKNqwBrnM/PSR9f19Zxz5eybG8h9+ck6xesjz6RYsyYMf4hmj59ujRo0MCbznZfK8b7fdxAnY+U0Eic+rl+P3JdXhRZ8HzMFpiOKiPJ+YUGWJP0i3v/S9LLLjvO+azluP58b9ctOG7/26o0AVYtJ41/jwi2Lzhd2dobbD/TCCCAAAIIIIAAAggggAACCCCAAAIIIIBAmgUqbIBV0e0/UG/fvt17zGPaDkZpvqC4//775eCDD/aakO2LVbuN2tPQqaeeKocccog0adJEqlWrZi/2xqOCWUn6Bb+AmjdvnkycOLFE3cp6RmmOR64egK+66io544wz/Caos26jYd2wQQO87dq18xdlC7CaleIcX1OGq9diaG9YgFW/ZJwyZYo8/fTTUqtWLZ8jV6/Arq8Pu7wNGzbIZZdd5tclbGTu3LlSvXp1b1ExBljtAG4+97NWrVp514+xWLFihQwbNsxMJvJa2jpmq0QS14fL+5XW3WV77fM5yffftARAiuH4Zjs/S7ustOdKaa5fF58P7PPPxf3ULs/1+eyivaU9fmlZPw0BVrVI8vi6tnZxvpRVewu9PydZP7v3+h9//FF69OjhHaJGjRrJ1KlT/cOlT9/QHgbDhmK838cN1IU5uJwXp36u349clxflVOj1EVWe6/mFBliT9HNx/3PtFFZenPNZy3P9+T6sjmaefb8tbYDVlJGmv0eYOkW9Vrb2RjkwHwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQSKNAhQ6w2l9Sag+s3bt3T90xsL+gyPVI7BkzZkj9+vW9NuQT+Apr7DHHHCOXXnqpaA9H9vD44497AT57XpJ+af0Cyj4ecXr0Ukd9JLz20qSDHq8hQ4aIPuY2atDeWhs3buwvzifA6q/8z5HSHN/gtnGni6G9wQCr/UWd9oB71113SZUqVXwKDTPodRA2uL4+7B6LvvnmG+nVq1fYbv159pejxRhgfeihh6Rp06Z+ezp37uyPh4107NhRRo4c6S968803vd6j/RkJjEyePDnjXtmnTx/ZsmVLQXtK4vpweb/SRrlsr+vrIwo9GADR4MQTTzwRtXpi84vh+LpsfJLXr4vPB67vp0mezy7aGzy2+hmvefPmwdlez+/2I9NLrFDGM9ISYE3y+LomdXG+lFV7C70/J1m/6667Tk488UT/sIwdO1YWLlxY4nHYDz74oLz88sv+evZIMd7v7c+MS5culREjRthNKvfxOPVz/X7kurwo3EKvj6jyXM8vNMCapJ+L+59rp7Dy4pzPWp7rz/dhdTTzXAQ6TVnmtTz/HmHqEPVa2dob5cB8BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgjQIVOsAaDATmCkiVxwGye1HUx8r37ds3shr2F7qFBlhN4RoE00CYGewgn5mXpF9av4By+YWRXdbmzZvlwgsvNLShr/a5oCsUEmA1BedzfM26rl6Lob3BAKuGEzSkYAYNd9tB9z179sigQYNEe+IKDq6vD7vHIt1v165dg7v0p4OP2E1DgLW0PaLecccdoj32mOHWW2+ox1E3AABAAElEQVSVBQsWmMkSr/3795cuXbr488siqHj99dfL8ccf7+9Tj7lep4UMSVwfdplxA/faJpftdX19RJm3bt06o/fu//qv/5Lf/e53UasnNt8+Fq7u93aZLo6vy8Ynef26+Hzg+n6a5Pnsor3BY6vvawcddFBwtuT6nFlig4RnpCXAmuTxdU3o4nwpq/YWen9Osn4tW7bMeI/QQPeoUaMyfnSW6zOYfW8ulvu9Hagr7ec11+dwWHlx6uf6/ch1eWHt1XmFXh9R5bmeX2iANUk/F/c/105h5cU5n7U8+x6T9Oe/JAKdxqQ8/h5h9h31msb23nbbbSWekvTOO+9E/og2qm3MRwABBBBAAAEEEEAAAQQQQAABBBBAAAEEil2gQgdYNRB11FFH+ceoLHrs83eW54j2FFevXj1vbX3EfM+ePUO31N4h7777bn9Z3ABrgwYNZPr06X55Yb3xJOmX1i+gXH5hZH959cUXX0i/fv187+DIOeecIxdddFHG7DgB1nyOb8bOHEwUQ3tzBViVIdhz0LZt2+SCCy4oIeT6+ggGjqZMmSJqGjaMHj1aTjjhBH9ReQVY7S+3v/rqK7nkkkv8OuUaufLKK+XMM8/0V1u+fLkMHz7cnw6OzJo1S+rUqePPHj9+vMyfP9+fTmIkeF3u2LFDzj333IJ2lcT14fJ+pY1y2V7X10c2dPvLaBNGyrZ+EsuK4fi6bHeS16+Lzweu76dJns8u2hs8tsH2m+UEWP+3R3z7xxBqk+TxNfauXl2cL2XZ3kLuz0nXz+5Bdffu3TJ48GCvB3JzjML+TWSW6Wsx3u/jfF6z257UeJz6uX4/cl1eNrNCro9s5blcZh+TdevWyRVXXJFX8Un6ubj/5dWImCvZdqX995Hu2vXn+2zNsc/BsB80Z9s217Ly+HtErjqlsb12nUz90/Z5zdSLVwQQQAABBBBAAAEEEEAAAQQQQAABBBBAIEmBCh1g1S85fv/73/uPBNfQ5+WXXy4aJsw1aC+M+uVD0kPwkc36pW1YL4T33Xef/OxnP/OrExVg1Z4Z9fHjW7du9dcNGznttNO8R9qbZcGeKHV+kn5p/QLK5RdGTz/9tNSqVcsj1i/Iu3XrZrhLvNq965qFYQFWl8fX7MfVazG0N58A63777SdTp07N6AlFHy+rj5m1B9fXR/Ca3LhxY4lQs9m//cWoziuvAKsdwM/VY5mpu3ndf//95dFHHzWTovc0/XJ8/fr1/jwzor1Fay8+ZsgW9jfruHoNXpth98rgvvTL+wceeCBjdhLXh8v7lamsq/a6vj5M/cJe7esh1702bHsX84rl+Lpoq5aR5PXr4vOB6/tpkuezi/YGjysB1kyRe++9V1q1auXNDPv8nOTxzaxJ/CkX50tZtreQ+3PS9Rs4cKCcddZZ/sHYsGGDNGnSxJ/WHiTfeustfzo4Uoz3+zif14LtT2I6Tv1cvx+5Li+bVyHXR7byXC6z61aaAGuSfi7ufy6NosqKcz5rmUl8vo+qqx2ezDfAmua/R0S108xPY3vtOpl6EmA1ErwigAACCCCAAAIIIIAAAggggAACCCCAQGUSqNABVj2Q11xzjZx00kn+MdWQ1ezZs0W/WAgO+qWIhgY7dOjghdf0C+/XX389uJrTae315/TTT/fL/P7772XEiBGyevVqf964cePk8MMP96d1JOwLeJ1vgq66vT5CV3udDQ5HHnmkjBkzJiOgp+E8DekFh6T80voFlMsvjO6//345+OCDfVI9Jvo4ento27at1/NXjRo17NneeFiA1fXxLbHTGDOKob35BFiV4JRTTpFhw4ZlaEyYMEHeeOONjHmurw87FKE7+uSTTzKC5tpbswaT7J5Idb3yCrDaoSCth4ZAtGdUfTxtPsOdd94pRxxxhL/qrl27vKCqvf2pp57qGVSpUsVfT+/hM2bM8KeTHNH9Dx06NGMXH3/8sVx99dUZ83RC3zs0aLvPPvuIBmTWrFnjr5PE9eHyfmUq6qq9Wp7r68PUMfh61113yWG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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An Opus-specific problem\n",
    "\n",
    "When working with Opus and tools, the model often outputs its thoughts in `<thinking>` or `<reflection>` tags before actually responding to a user.  You can see an example of this in the screenshot below: \n",
    "\n",
    "![conversation7.png](attachment:conversation7.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This thinking process tends to lead to better results, but it obviously makes for a terrible user experience. We don't want to expose that thinking logic to a user! The easiest fix here is to explicitly ask the model output its user-facing response in a particular set of XML tags. We start by updating the system prompt:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "system_prompt = \"\"\"\n",
    "You are a customer support chat bot for an online retailer called TechNova. \n",
    "Your job is to help users look up their account, orders, and cancel orders.\n",
    "Be helpful and brief in your responses.\n",
    "You have access to a set of tools, but only use them when needed.  \n",
    "If you do not have enough information to use a tool correctly, ask a user follow up questions to get the required inputs.\n",
    "Do not call any of the tools unless you have the required data from a user. \n",
    "\n",
    "In each conversational turn, you will begin by thinking about your response. \n",
    "Once you're done, you will write a user-facing response. \n",
    "It's important to place all user-facing conversational responses in <reply></reply> XML tags to make them easy to parse.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a look at an example conversation output: \n",
    "\n",
    "![conversation8.png](./images/conversation8.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Claude is now responding with `<reply>` tags around the actual user-facing response.  All we need to do now is extract the content between those tags and make sure that's the only part of the response we actually print to the user.  Here's an updated `start_chat` function that does exactly that!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def extract_reply(text):\n",
    "    pattern = r'<reply>(.*?)</reply>'\n",
    "    match = re.search(pattern, text, re.DOTALL)\n",
    "    if match:\n",
    "        return match.group(1)\n",
    "    else:\n",
    "        return None    \n",
    "    \n",
    "def simple_chat():\n",
    "    system_prompt = \"\"\"\n",
    "    You are a customer support chat bot for an online retailer called TechNova. \n",
    "    Your job is to help users look up their account, orders, and cancel orders.\n",
    "    Be helpful and brief in your responses.\n",
    "    You have access to a set of tools, but only use them when needed.  \n",
    "    If you do not have enough information to use a tool correctly, ask a user follow up questions to get the required inputs.\n",
    "    Do not call any of the tools unless you have the required data from a user. \n",
    "\n",
    "    In each conversational turn, you will begin by thinking about your response. \n",
    "    Once you're done, you will write a user-facing response. \n",
    "    It's important to place all user-facing conversational responses in <reply></reply> XML tags to make them easy to parse.\n",
    "    \"\"\"\n",
    "\n",
    "    user_message = input(\"\\nUser: \")\n",
    "    messages = [{\"role\": \"user\", \"content\": [{\"text\": user_message}]}]\n",
    "\n",
    "    while True:\n",
    "        #If the last message is from the assistant, get another input from the user\n",
    "        if messages[-1].get(\"role\") == \"assistant\":\n",
    "            user_message = input(\"\\nUser: \")\n",
    "            messages.append({\"role\": \"user\", \"content\": [{\"text\": user_message}]})\n",
    "\n",
    "        converse_api_params = {\n",
    "            \"modelId\": modelId,\n",
    "            \"system\": [{\"text\": system_prompt}],\n",
    "            \"messages\": messages,\n",
    "            \"inferenceConfig\": {\"maxTokens\": 4096},\n",
    "            \"toolConfig\":toolConfig,\n",
    "        }\n",
    "\n",
    "        response = bedrock_client.converse(**converse_api_params)\n",
    "\n",
    "        messages.append({\"role\": \"assistant\", \"content\": response['output']['message']['content']})\n",
    "\n",
    "        #If Claude stops because it wants to use a tool:\n",
    "        if response['stopReason'] == \"tool_use\":\n",
    "            tool_use = response['output']['message']['content'][-1] #Naive approach assumes only 1 tool is called at a time\n",
    "            tool_id = tool_use['toolUse']['toolUseId']\n",
    "            tool_name = tool_use['toolUse']['name']\n",
    "            tool_input = tool_use['toolUse']['input']\n",
    "\n",
    "            print(f\"======Claude wants to use the {tool_name} tool======\")\n",
    "\n",
    "            #Actually run the underlying tool functionality on our db\n",
    "            tool_result = process_tool_call(tool_name, tool_input)\n",
    "\n",
    "            #Add our tool_result message:\n",
    "            messages.append({\n",
    "                \"role\": \"user\",\n",
    "                \"content\": [\n",
    "                    {\n",
    "                        \"toolResult\": {\n",
    "                            \"toolUseId\": tool_id,\n",
    "                            \"content\": [\n",
    "                                {\"text\": str(tool_result)}\n",
    "                            ]\n",
    "                        }\n",
    "                    }\n",
    "                ]\n",
    "            })\n",
    "        else: \n",
    "            #If Claude does NOT want to use a tool, just print out the text reponse\n",
    "            model_reply = extract_reply({response['output']['message']['content'][0]['text']})\n",
    "            print(\"\\nTechNova Support: \" + f\"{model_reply}\" )\n",
    "\n",
    "\n",
    "# Start the chat!!\n",
    "# simple_chat()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a screenshot showing the impact of the above change: \n",
    "\n",
    "![conversation9.png](./images/conversation9.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final version\n",
    "\n",
    "Here's a screenshot of a longer conversation with a version of this script that colorizes the output.\n",
    "\n",
    "![conversation10.png](./images/conversation10.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Closing notes\n",
    "\n",
    "This is an educational demo made to illustrate the tool use workflow.  This script and prompt are NOT ready for production. The assistant responds with things like \"You can find the order ID on the order confirmation email\" without actually knowing if that's true. In the real world, we would need to provide Claude with lots of background knowledge about our particular company (at a bare minimum).  We also need to rigorously test the chatbot and make sure it behaves well in all situations.  Also, it's probably a bad idea to make it so easy for anyone to cancel an order! It would be pretty easy to cancel a lot of people's orders if you had access to their email, username, or phone number. In the real world, we would probably want some form of authentication.  Long story short: this is a demo and not a complete chatbot implementation!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    "* Add functionality to our assistant (and underlying `FakeDatabase` class) that allows a user to update their email and phone number.\n",
    "* Define a new tool called `get_user_info` that combines the functionality of `get_user` and `get_customer_orders`. This tool should take a user's email, phone, or username as input and return the user's information along with their order history all in one go.\n",
    "* Add error handling and input validation!\n",
    "\n",
    "\n",
    "#### Bonus\n",
    "* Update the chatbot to use an ACTUAL database instead of our `FakeDatabase` class."
   ]
  }
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